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1,913	2,014	"The Thought Experiment \| MIT Technology Review"	"https://www.technologyreview.com/s/528141/the-thought-experiment"	"Featured Topics Newsletters Events Podcasts Featured Topics Newsletters Events Podcasts The Thought Experiment By Antonio Regalado archive page I was about 15 minutes late for my first phone call with Jan Scheuermann. When I tried to apologize for keeping her waiting, she stopped me. “I wasn’t just sitting around waiting for you, you know,” she said, before catching herself. “Well, actually I was sitting around.” Scheuermann, who is 54, has been paralyzed for 14 years. She had been living in California and running a part-time business putting on mystery-theater dinners, where guests acted out roles she made up for them. “Perfectly healthy, married, with two kids,” she says. One night, during a dinner she’d organized, it felt as if her legs were dragging behind her. “I chalked it up to being a cold snowy night, but there were a couple of steps in the house, and boy, I was really having trouble,” she says. Anguished months of doctor’s visits and misdiagnoses followed. A neurologist said she had multiple sclerosis. By then, she was using an electric wheelchair and “fading rapidly.” She thought she was dying, so she moved home to Pittsburgh, where her family could take care of her children. Eventually she was diagnosed with a rare disease called spinocerebellar degeneration. She can feel her body, but the nerves that carry signals out from her brain no longer work. Her brain says “Move,” but her limbs can’t hear. Two and a half years ago, doctors screwed two ports into Scheuermann’s skull (she calls them “Lewis and Clark”). The ports allow researchers to insert cables that connect with two thumbtack-size implants in her brain’s motor cortex. Two or three times a week, she joins a team of scientists at the University of Pittsburgh and is plugged into a robotic arm that she controls with her mind. She uses it to move blocks, stack cones, give high fives, and pose for silly pictures, doing things like pretending to knock out a researcher or two. She calls the arm Hector. Scheuermann, who says that in her dreams she is not disabled, underwent brain surgery in 2012 after seeing a video of another paralyzed patient controlling a robotic arm with his thoughts. She immediately applied to join the study. During the surgery, doctors used an air gun to fire the two tiny beds of silicon needles, called the Utah Electrode Array, into her motor cortex, the slim strip of brain that runs over the top of the head to the jaws and controls voluntary movement. She awoke from the surgery with a pounding headache and “the worst case of buyer’s remorse.” She couldn’t believe she’d had voluntary brain surgery. “I thought, Please, God, don’t let this be for nothing. My greatest fear was that it wouldn’t work,” she says. But within days, she was controlling the robotic arm, and with unexpected success: “I was moving something in my environment for the first time in years. It was gasp-inducing and exciting. The researchers couldn’t wipe the smile off their faces for weeks either.” Scheuermann is one of about 15 to 20 paralyzed patients who have joined long-term studies of implants that can convey information from the brain to a computer. She is the first subject at Pittsburgh. Nine others, including people in the advanced stages of ALS, have undergone similar tests in a closely related study, called BrainGate. Another four “locked-in” patients, unable to move or speak, have regained some ability to communicate thanks to a different type of electrode developed by a Georgia company called Neural Signals. A third of these patients have undergone surgery since 2011, when the U.S. Food and Drug Administration said it would loosen rules for testing “truly pioneering technologies” such as brain-machine interfaces. More human experiments are under way. One, at Caltech, wants to give a patient “autonomous control over the Google Android tablet operating system.” A team at Ohio State University, in collaboration with the R&D organization Battelle, put an implant in a patient in April with the intention of using the patient’s brain signals to control stimulators attached to his arm. Battelle describes the idea as “reanimating a paralyzed limb under voluntary control by the participant’s thoughts.” These nervy first-of-a-kind studies rely on the fact that recording the electrical activity of a few dozen cells in the brain can give a fairly accurate picture of where someone intends to move a limb. “We are technologically limited to sampling a couple of hundred neurons, from billions in your brain, so it’s actually amazing they can get a signal out at all,” says Kip Ludwig, director of the neural engineering program at the National Institute of Neurological Disorders and Stroke. The technology being used at Pittsburgh was developed in physiology labs to study animals, and it is plainly still experimental. The bundled wires lead from Scheuermann’s cranium to a bulky rack of signal processors, amplifiers, and computers. The nine-pound robotic arm, paid for by the military, has a dexterous hand and fingers that can make lifelike movements, but it is finicky, breaks frequently, and is somewhat dangerous. When things don’t work, graduate students hunt among tangles of wires for loose connections. John Donoghue, the Brown University neuroscientist who leads the longer-running BrainGate study, compares today’s brain-machine interfaces to the first pacemakers. Those early models also featured carts of electronics, with wires punched through the skin into the heart. Some were hand-cranked. “When you don’t know what is going on, you keep as much as possible on the outside and as little as possible on the inside,” says Donoghue. Today, though, pacemakers are self-contained, powered by a long-lasting battery, and installed in a doctor’s office. Donoghue says brain-machine interfaces are at the start of a similar trajectory. For brain-controlled computers to become a medical product, there has to be an economic rationale, and the risks must be offset by the reward. So far, Scheuermann’s case has come closest to showing that these conditions can be met. In 2013, the Pittsburgh team reported its work with Scheuermann in the medical journal the Lancet. After two weeks, they reported, she could move the robot arm in three dimensions. Within a few months, she could make seven movements, including rotating Hector’s hand and moving the thumb. At one point, she was filmed feeding herself a bite of a chocolate bar, a goal she had set for herself. The researchers tried to show that they were close to something practical—helping with so-called “tasks of daily living” that most people take for granted, like brushing teeth. During the study, Scheuermann’s abilities were examined using the Action Research Arm Test, the same kit of wooden blocks, marbles, and cups that doctors use to evaluate hand dexterity in people with recent injuries. She scored 17 out of 57, or about as well as someone with a severe stroke. Without Hector, Scheuermann would have scored zero. The findings made 60 Minutes. At first it was “success, success, success,” but Scheuermann says no one told her the implant might stop working. Gradually, it is recording from fewer neurons. Her control over the robot arm is weakening. Since the TV cameras went away, however, some of the shortcomings of the technology have become apparent. At first Scheuermann kept demonstrating new abilities. “It was success, success, success,” she says. But controlling Hector has become harder. The reason is that the implants, over time, stop recording. The brain is a hostile environment for electronics, and tiny movements of the array may build up scar tissue as well. The effect is well known to researchers and has been observed hundreds of times in animals. One by one, fewer neurons can be detected. Scheuermann says no one told her. “The team said that they were expecting loss of neuron signals at some point. I was not, so I was surprised,” she says. She now routinely controls the robot in only three to five dimensions, and she has gradually lost the ability to open and close its thumb and fingers. Was this at all like her experience of becoming paralyzed? I asked her the question a few days later by e-mail. She replied in a message typed by an aide who stays with her most days: “I was disappointed that I would probably never do better than I had already done, but accepted it without anger or bitterness.” Reanimation The researcher who planned the Pittsburgh experiment is Andrew Schwartz, a lean Minnesotan whose laboratory occupies a sunlit floor dominated by three gray metal towers of equipment that are used to monitor monkeys in adjacent suites. Seen on closed-circuit TVs, the scenes from inside the experimental rooms defy belief. On one screen, a metal wheel repeatedly rotates, changing the position of a bright orange handle. After each revolution, an outsize robotic hand reaches up from the edge of the screen to grab the handle. Amid the spinning machinery, it’s easy to miss the gray and pink face of the rhesus monkey that is controlling all this from a cable in its head. The technology has its roots in the 1920s, with the discovery that neurons convey information via electrical “spikes” that can be recorded with a thin metal wire, or electrode. By 1969, a researcher named Eberhard Fetz had connected a single neuron in a monkey’s brain to a dial the animal could see. The monkey, he discovered, learned to make the neuron fire faster to move the dial and get a reward of a banana-flavored pellet. Although Fetz didn’t realize it at the time, he had created the first brain-machine interface. Schwartz helped extend that discovery 30 years ago when physiologists began recording from many neurons in living animals. They found that although the entire motor cortex erupts in a blaze of electrical signals when an animal moves, a single neuron will tend to fire fastest in connection with certain movements—say, moving your arm left or up, or bending the elbow—but less quickly otherwise. Record from enough neurons and you can get a rough idea of what motion a person is making, or merely intending. “It’s like a political poll, and the more neurons you poll, the better the result,” he says. The 192 electrodes on Scheuermann’s two implants have recorded more than 270 neurons at a time, which is the most ever simultaneously measured from a human being’s brain. Schwartz says this is why her control over the robot has been so good. The neuronal signals are interpreted by software called a decoder. Over the years, scientists built better and better decoders, and they tried more ambitious control schemes. In 1999, the Duke University neuroscientist Miguel Nicolelis trained a rat to swing a cantilever with its mind to obtain a reward. Three years later, Donoghue had a monkey moving a cursor in two dimensions across a computer screen, and by 2004 his BrainGate team had carried out the first long-term human test of the Utah array, showing that even someone whose limbs had been paralyzed for years could control a cursor mentally. By 2008, Schwartz had a monkey grabbing and feeding itself a marshmallow with a robotic hand. Some paralyzed people are starting to look at electronics as their best chance for recovery. “I’ll take it! Cut off my dead arm and give me a robotic one that I can FEEL with please!” wrote one. Scheuermann has been able to quickly attempt many new tasks. She has been asked to control two robot arms at once and lift a box (“I only managed it once or twice,” she says). Some results are strange: Scheuermann is able to close Hector’s fingers around a plastic cone, but often only if she shuts her eyes first. Is the presence of the cone somehow reflected in the neurons’ firing patterns? Schwartz has spent months trying to figure it out. Behind such points of uncertainty may lie major discoveries about how the brain prepares and executes actions. Scheuermann once had her aide dress her in stick-on rat whiskers and a fuzzy tail to greet researchers. It was a darkly humorous way of acknowledging that these experiments depend on human volunteers. “They are not nearly as hard to train as these guys,” Schwartz says, jerking a thumb to the row of monkey rooms. These volunteers are trapped; some of them desperately hope science can provide an escape. Realistically, that is unlikely in their lifetimes. The first BrainGate volunteer was a 25-year-old named Matt Nagle, who had breathed through a ventilator since his spinal cord was severed during a knife fight. He was able to move a cursor on a screen in 2004. But Nagle also wanted to commit suicide and tried to get others to help him do it, according to The Man with the Bionic Brain , a book written by his doctor. He died of an infection in 2007. On online chat boards where paralyzed people trade hopeful news about possible cures, like stem cells, some dismiss brain-machine interfaces as wacky. Others are starting to think it’s their best chance. “I’ll take it! Cut off my dead arm and give me a robotic one that I can FEEL with please!” wrote one. Schwartz says he hopes to generate physical sensations from the robotic arm this year, if he can find another quadriplegic volunteer. Like Scheuermann, the next patient will receive two arrays in the motor cortex to control the robotic arm. But Schwartz says surgeons will place two additional implants into the volunteer’s sensory cortex; these will receive signals from pressure sensors attached to the robotic fingertips. Studies by Nicolelis’s Duke laboratory proved recently that animals do sense and respond to such electrical inputs. “We don’t know if the subject will feel it as touch,” says Schwartz. “It’s very crude and simplistic and an undoubtedly incorrect set of assumptions, but you can’t ask the monkey what he just felt. We think it will be a new scientific finding. If the patient can say how it feels, that is going to be news.” Another key aim, shared by Schwartz and the BrainGate researchers, is to connect a volunteer’s motor cortex to electrodes placed in his or her limbs, which would make the muscles contract—say, to open and close a hand. In April, the Ohio surgeons working with Battelle announced that they would be the first to try it. They put a brain implant in a man with a spinal-cord injury. And as soon as the patient recovers, says Battelle, they’ll initiate tests to “reanimate” his fingers, wrist, and hand. “We want to help someone gain control over their own limb,” says Chad Bouton, the engineer in charge of the project, who previously collaborated with the BrainGate group. “Can someone pick up a TV remote and change the channel?” Although Battelle has not won approval from regulators to attempt it, Bouton says the obvious next step is to try a bidirectional signal to and from a paralyzed limb, combining control and sensation. Interface Problems Brain-machine interfaces may seem as if they’re progressing quickly. “If you fast-forward from the first video of that monkey to someone moving a robot in seven dimensions, picking things up, putting them down, it’s pretty dramatic,” says Lee Miller, a neurophysiologist at Northwestern University. “But what hasn’t changed, literally, is the array. It’s the Stanley Steamer of brain implants. Even if you demonstrate control, it’s going to peter out in two to three years. We need an interface that will last 20 years before this can be a product.” The Utah array was developed in the early 1990s as a way to record from the cortex, initially of cats, with minimal trauma to the brain. It’s believed that scar tissue builds up around the needle-like recording tips, each 1.5 millimeters long. If that interface problem is solved, says Miller, he doesn’t see any reason why there couldn’t be 100,000 people with brain implants to control wheelchairs, computer cursors, or their own limbs. Schwartz adds that if it’s also possible to measure from enough neurons at once, someone could even play the piano with a thought-controlled robotic arm. Researchers are pursuing several ideas for improving the brain interface. There are efforts to develop ultrathin electrodes, versions that are more compatible with the body, or sheets of flexible electronics that could wrap around the top of the brain. In San Francisco, doctors are studying whether such surface electrodes, although less precise, could be used in a decoder for speech, potentially allowing a person like Stephen Hawking to speak via a brain-computer interface. In an ambitious project launched last year at the University of California, Berkeley, researchers are trying to develop what they call “neural dust.” The goal is to scatter micron-size piezoelectric sensors throughout the brain and use reflected sound waves to capture electrical discharges from nearby neurons. Jose Carmena, a Berkeley researcher who, like Schwartz, works with monkeys to test the limits of thought control, now meets weekly with a group of a dozen scientists to outline plans for better ways to record from neurons. But whatever they come up with would have to be tested in animals for years before it could be tried in a person. “I don’t think the Utah array is going to become the pacemaker of the brain,” he says. “But maybe what we end up using is not that different. You don’t see the newest computer in space missions. You need the most robust technology. It’s the same kind of thing here.” Numbers Game To succeed, any new medical device needs to be safe, useful, and economically viable. Right now, brain-machine interfaces don’t meet these requirements. One problem is the riskiness of brain surgery and the chance of infection. At Brown, Donoghue says the BrainGate team is almost finished developing a wireless transmitter, about the size of a cigarette lighter, that would go under a person’s skin and cut the infection risk by getting rid of the pedestals and wires that make brain-machine interfaces so unwieldy. Donoghue says that with a wireless system, implants could be a realistic medical option soon. But that raises another tricky problem: what will patients control? The arm Scheuermann controls is still a hugely expensive prototype, and it often breaks. She worries that not everyone could afford one. Instead, Leigh Hochberg, a neurologist at Massachusetts General Hospital who runs the BrainGate study with Donoghue, thinks the first users will probably be “locked-in” patients who can neither move nor speak. Hochberg would consider it a “breakthrough” to afford such patients reliable thought control over a computer mouse. That would let them type out words or change the channel on a television. Yet even locked-in patients can often move their eyes. This means they have simpler ways to communicate, like using an eye tracker. A survey of 61 ALS patients by the University of Michigan found that about 40 percent of them would consider undergoing surgery for a brain implant, but only if it would let them communicate more than 15 words a minute (a fifth of the people who responded to the survey were already unable to speak). BrainGate has not yet reported speeds that high. Without a clearly defined product to shoot for, no large company has jumped in to create a neuroprosthetic for quadriplegics. A startup went out of business after raising more than $30 million. All the pieces of the technology “have at some level been solved,” says Andy Gotshalk, CEO of Blackrock Microsystems, which manufactures the Utah array and has acquired some of the BrainGate technology. “But if you ask me what is the product—is it a prosthetic arm or is it a wheelchair you control?—then I don’t know. There is a high-level product in mind, which is to make life for quadriplegics a lot easier. But exactly what it would be hasn’t been defined. It’s just not concrete. The scientists are getting some high-level publications, but I have to think about the business plan, and that is a problem.” Without a clear product to shoot for, no large company has ever jumped in. And the risks for a business are especially high because there are relatively few patients with complete quadriplegia—about 40,000 in the U.S.—and even fewer with advanced ALS. A company Donoghue created, Cyberkinetics, went out of business after raising more than $30 million. Researchers instead get by on small grants that are insignificant compared with a typical commercial effort to develop a new medical device, which can cost $100 million. “There is not a single company out there willing to put the money in to create a neuroprosthetic for quadriplegics, and the market is not big enough for a venture capitalist to get in,” says Gotshalk. “The numbers don’t add up.” Others think the technology behind brain-machine interfaces may have unexpected applications, far removed from controlling robot arms. Many researchers, including Carmena and the team at Battelle, are trying to determine whether the interfaces might help rehabilitate stroke patients. Because they form a large market, it “would be a game changer,” Carmena says. Some of the recording technologies could be useful for understanding psychiatric diseases like depression or obsessive-compulsive disorder. In Scheuermann’s case, at least, her brain-machine interface has proved to be powerful medicine. When she first arrived at Pittsburgh, her doctors say, her affect was flat, and she didn’t smile. But being part of the experiment energized her. “I was loving it. I had coworkers for the first time in 20 years, and I felt needed,” she says. She finished dictating a mystery novel, Sharp as a Cucumber , that she’d started before she became ill and published it online. Now she’s working on a second one. Scheuermann told me she’d like to have a robotic arm at home. She’d be able to open the door, roll onto her lawn, and talk to neighbors. Maybe she’d open the refrigerator and grab a sandwich that her aide had prepared for her. Our call was ending. The moment was awkward. I could hang up the phone, but she couldn’t. Her husband had gone out shopping. Hector was back in the lab. She was alone and couldn’t move. “That’s all right,” Scheuermann said. “I’ll just let the phone slip to the floor. Good-bye.” hide by Antonio Regalado Share linkedinlink opens in a new window twitterlink opens in a new window facebooklink opens in a new window emaillink opens in a new window This story was part of our July/August 2014 issue. Popular This new data poisoning tool lets artists fight back against generative AI Melissa Heikkilä Everything you need to know about artificial wombs Cassandra Willyard Deepfakes of Chinese influencers are livestreaming 24/7 Zeyi Yang How to fix the internet Katie Notopoulos Keep Reading Most Popular This new data poisoning tool lets artists fight back against generative AI The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models. By Melissa Heikkilä archive page Everything you need to know about artificial wombs Artificial wombs are nearing human trials. But the goal is to save the littlest preemies, not replace the uterus. By Cassandra Willyard archive page Deepfakes of Chinese influencers are livestreaming 24/7 With just a few minutes of sample video and $1,000, brands never have to stop selling their products. By Zeyi Yang archive page How to fix the internet If we want online discourse to improve, we need to move beyond the big platforms. By Katie Notopoulos archive page Stay connected Illustration by Rose Wong Get the latest updates from MIT Technology Review Discover special offers, top stories, upcoming events, and more. Enter your email Thank you for submitting your email! It looks like something went wrong. We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at [email protected] with a list of newsletters you’d like to receive. 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1,914	2,020	"It's Called Artificial Intelligence—but What Is Intelligence? \| WIRED"	"https://www.wired.com/story/its-called-artificial-intelligence-but-what-is-intelligence"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Will Knight Business It's Called Artificial Intelligence—but What Is Intelligence? Illustration: Elena Lacey Save this story Save Save this story Save End User Research Sector Research Technology Machine learning Robotics Elizabeth Spelke, a cognitive psychologist at Harvard, has spent her career testing the world’s most sophisticated learning system—the mind of a baby. Gurgling infants might seem like no match for artificial intelligence. They are terrible at labeling images, hopeless at mining text, and awful at videogames. Then again, babies can do things beyond the reach of any AI. By just a few months old, they've begun to grasp the foundations of language, such as grammar. They've started to understand how the physical world works , how to adapt to unfamiliar situations. Yet even experts like Spelke don't understand precisely how babies—or adults, for that matter—learn. That gap points to a puzzle at the heart of modern artificial intelligence: We're not sure what to aim for. Related Stories Check Mate Will Knight Blind Spot Will Knight The A.I. Issue Clive Thompson Consider one of the most impressive examples of AI, AlphaZero , a program that plays board games with superhuman skill. After playing thousands of games against itself at hyperspeed, and learning from winning positions, AlphaZero independently discovered several famous chess strategies and even invented new ones. It certainly seems like a machine eclipsing human cognitive abilities. But AlphaZero needs to play millions more games than a person during practice to learn a game. Most tellingly, it cannot take what it has learned from the game and apply it to another area. To some members of the AI priesthood, that calls for a new approach. “What makes human intelligence special is its adaptability—its power to generalize to never-seen-before situations,” says François Chollet, a well-known AI engineer and the creator of Keras, a widely used framework for deep learning. In a November research paper, he argued that it's misguided to measure machine intelligence solely according to its skills at specific tasks. “Humans don't start out with skills; they start out with a broad ability to acquire new skills,” he says. “What a strong human chess player is demonstrating isn't the ability to play chess per se, but the potential to acquire any task of a similar difficulty. That's a very different capability.” Chollet posed a set of problems designed to test an AI program's ability to learn in a more generalized way. Each problem requires arranging colored squares on a grid based on just a few prior examples. It's not hard for a person. But modern machine-learning programs—trained on huge amounts of data —cannot learn from so few examples. As of late April, more than 650 teams had signed up to tackle the challenge; the best AI systems were getting about 12 percent correct. A self-driving car cannot intuit from common sense what will happen if a truck spills its load. It isn't yet clear how humans solve these problems, but Spelke's work offers a few clues. For one thing, it suggests that humans are born with an innate ability to quickly learn certain things, like what a smile means or what happens when you drop something. It also suggests we learn a lot from each other. One recent experiment showed that 3-month-olds appear puzzled when someone grabs a ball in an inefficient way, suggesting that they already appreciate that people cause changes in their environment. Even the most sophisticated and powerful AI systems on the market can't grasp such concepts. A self-driving car , for instance, cannot intuit from common sense what will happen if a truck spills its load. Josh Tenenbaum, a professor in MIT's Center for Brains, Minds & Machines, works closely with Spelke and uses insights from cognitive science as inspiration for his programs. He says much of modern AI misses the bigger picture, likening it to a Victorian-era satire about a two-dimensional world inhabited by simple geometrical people. “We're sort of exploring Flatland —only some dimensions of basic intelligence,” he says. Tenenbaum believes that, just as evolution has given the human brain certain capabilities, AI programs will need a basic understanding of physics and psychology in order to acquire and use knowledge as efficiently as a baby. And to apply this knowledge to new situations, he says, they'll need to learn in new ways—for example, by drawing causal inferences rather than simply finding patterns. “At some point—you know, if you're intelligent—you realize maybe there's something else out there,” he says. This article appears in the June issue. Subscribe now. Let us know what you think about this article. Submit a letter to the editor at [email protected]. Is the Brain a Useful Model for Artificial Intelligence ? 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1,915	2,019	"Cruise's $1 Billion Infusion Shows the Stakes in Self-Driving Tech \| WIRED"	"https://www.wired.com/story/cruises-billion-infusion-shows-stakes-self-driving-tech"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Aarian Marshall Transportation Cruise's $1 Billion Infusion Shows the Stakes in Self-Driving Tech GM's Cruise unit has raised $7.25 billion toward developing self-driving technology in the past year. Elijah Nouvelage/Reuters Save this story Save Save this story Save For an industry that has yet to scale a commercial product, the folks building self-driving cars sure have raised a lot of money. The latest eye-popping investment comes via Cruise, the San Francisco-based autonomous vehicle unit that is mostly owned by General Motors. The company announced Tuesday that it had raised $1.15 billion, at a valuation of $19 billion—roughly one-third of the valuation of GM, despite not having sold a single car. The infusion comes from one new investor, the global asset management firm T. Rowe Price Associates, plus existing partners GM, Honda, and the Softbank Vision Fund, which poured $2.25 billion into the self-driving unit a year ago. Cruise says it has raised $7.25 billion in the past year, which places it in the top tier of AV fund-raisers. Cruise will use the money to, in part, pick up more people. The company’s headcount sits at 1,200, as it ramps up plans to hire 200 engineers in Seattle. It plans to hire 1,000 engineers by the end of the year. In San Francisco, Cruise just hired a new human resources chief and signed a lease on new office digs to accommodate the growth. The company has previously said that it plans to launch a self-driving service this year. In a statement, Cruise CEO Dan Ammann acknowledged that the race to build self-driving tech is expensive. “Developing and deploying self-driving vehicles at massive scale is the engineering challenge of our generation,” he said. “Having deep resources to draw on as we pursue our mission is a critical competitive advantage.” Other companies also seem to think the tech is worth the expense. In just the past six months, a number of self-driving tech developers have announced very large investments. Argo AI, the autonomous-vehicle startup in which Ford has a majority stake, raised $1.7 billion from Volkswagen , at a $4 billion valuation. Uber’s self-driving unit raised $1 billion from a Japanse consortium that includes Softbank and Honda, valuing the company’s Advanced Technology Group at $7.3 billion. Softbank also pumped $940 million into the robotic delivery startup Nuro, at a $2.7 billion valuation. The startup Aurora announced a $530 million raise at a $2 billion valuation, with an alley-oop from self-driving investing newcomer Amazon. Electric-car maker Tesla, whose CEO, Elon Musk, has promised to produce fully functioning self-driving software by 2020 , announced it would raise $2.7 billion just last week. Another big raise is probably coming. In March, the Information reported that Google spinoff Waymo, the putative leader in self driving, intends to seek outside financing—and that Waymo is spending about $1 billion a year to develop autonomous vehicles. The larger implication is that building autonomous-vehicle tech costs a pretty penny. Most developers spend on pricey sensors, which help self-driving vehicles “see,” and on processors, software, cloud computing, and high-definition maps, which need to be constantly updated—all worth tens of thousands per vehicle. They not only must pay highly skilled (and highly bonused) engineers to make advancements in machine learning, but they also shell out for all of the operators who currently monitor self-driving technology as it's tested in all sorts of conditions, at all times of day. What’s more, developing this ambitious technology might take a few more years than prognosticators once promised—which means someone will have to keep writing checks for some time. “To the richest go the spoils” is not always a surefire maxim. But in the spendy world of autonomous vehicle development, money certainly can’t hurt. Portland is again blazing trails for open internet access Should I spend $1,000 on a smartphone? Combatting drug deaths with opioid vending machines Exquisite underwater photos to make you love the ocean 15 months of fresh hell inside Facebook 🎧 Things not sounding right? Check out our favorite wireless headphones , soundbars , and bluetooth speakers 📩 Want more? 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1,916	2,020	"Is the Brain a Useful Model for Artificial Intelligence? \| WIRED"	"https://www.wired.com/story/brain-model-artificial-intelligence"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Kelly Clancy Business Is the Brain a Useful Model for Artificial Intelligence? Photograph: John M. Lund/Getty Images Save this story Save Save this story Save Application Human-computer interaction End User Research Sector Research Technology Machine learning In the summer of 2009, the Israeli neuroscientist Henry Markram strode onto the TED stage in Oxford, England, and made an immodest proposal: Within a decade, he said, he and his colleagues would build a complete simulation of the human brain inside a supercomputer. They'd already spent years mapping the cells in the neocortex, the supposed seat of thought and perception. “It's a bit like going and cataloging a piece of the rain forest,” Markram explained. “How many trees does it have? What shapes are the trees?” Now his team would create a virtual rain forest in silicon, from which they hoped artificial intelligence would organically emerge. If all went well, he quipped, perhaps the simulated brain would give a follow-up TED talk, beamed in by hologram. Related Stories Wired Q&A Tom Simonite The A.I. Issue Shaun Raviv Sleep No More Kelly Clancy Markram's idea—that we might grasp the nature of biological intelligence by mimicking its forms —was rooted in a long tradition, dating back to the work of the Spanish anatomist and Nobel laureate Santiago Ramón y Cajal. In the late 19th century, Cajal undertook a microscopic study of the brain, which he compared to a forest so dense that “the trunks, branches, and leaves touch everywhere.” By sketching thousands of neurons in exquisite detail, Cajal was able to infer an astonishing amount about how they worked. He saw that they were effectively one-way input-output devices: They received electrochemical messages in treelike structures called dendrites and passed them along through slender tubes called axons, much like “the junctions of electric conductors.” Cajal's way of looking at neurons became the lens through which scientists studied brain function. It also inspired major technological advances. In 1943, the psychologist Warren McCulloch and his protégé Walter Pitts, a homeless teenage math prodigy, proposed an elegant framework for how brain cells encode complex thoughts. Each neuron, they theorized, performs a basic logical operation, combining multiple inputs into a single binary output: true or false. These operations, as simple as letters in the alphabet, could be strung together into words, sentences, paragraphs of cognition. McCulloch and Pitts' model turned out not to describe the brain very well, but it became a key part of the architecture of the first modern computer. Eventually, it evolved into the artificial neural networks now commonly employed in deep learning. These networks might better be called neural- ish. Like the McCulloch-Pitts neuron, they're impressionistic portraits of what goes on in the brain. Suppose you're approached by a yellow Labrador. In order to recognize the dog, your brain must funnel raw data from your retinas through layers of specialized neurons in your cerebral cortex, which pick out the dog's visual features and assemble the final scene. A deep neural network learns to break down the world similarly. The raw data flows from a large array of neurons through several smaller sets of neurons, each pooling inputs from the previous layer in a way that adds complexity to the overall picture: The first layer finds edges and bright spots, which the next combines into textures, which the next assembles into a snout, and so on, until out pops a Labrador. Despite these similarities, most artificial neural networks are decidedly un-brainlike, in part because they learn using mathematical tricks that would be difficult, if not impossible, for biological systems to carry out. Yet brains and AI models do share something fundamental in common: Researchers still don't understand why they work as well as they do. What computer scientists and neuroscientists are after is a universal theory of intelligence—a set of principles that holds true both in tissue and in silicon. What they have instead is a muddle of details. Eleven years and $1.3 billion after Markram proposed his simulated brain, it has contributed no fundamental insights to the study of intelligence. Part of the problem is something the writer Lewis Carroll put his finger on more than a century ago. Carroll imagined a nation so obsessed with cartographic detail that it kept expanding the scale of its maps—6 yards to the mile, 100 yards to the mile, and finally a mile to the mile. A map the size of an entire country is impressive, certainly, but what does it teach you? Even if neuroscientists can re-create intelligence by faithfully simulating every molecule in the brain, they won't have found the underlying principles of cognition. As the physicist Richard Feynman famously asserted, “What I cannot create, I do not understand.” To which Markram and his fellow cartographers might add: “And what I can create, I do not necessarily understand.” It's possible that AI models don't need to mimic the brain at all. Airplanes fly despite bearing little resemblance to birds. Yet it seems likely that the fastest way to understand intelligence is to learn principles from biology. This doesn't stop at the brain: Evolution's blind design has struck on brilliant solutions across the whole of nature. Our greatest minds are currently hard at work against the dim almost-intelligence of a virus, its genius borrowed from the reproductive machinery of our cells like the moon borrows light from the sun. Still, it's crucial to remember, as we catalog the details of how intelligence is implemented in the brain, that we're describing the emperor's clothes in the absence of the emperor. We promise ourselves, however, that we'll know him when we see him—no matter what he's wearing. KELLY CLANCY (@kellybclancy) is a neuroscientist at University College London and DeepMind. She wrote about fatal familial insomnia , a rare disease, in issue 27.02. This article appears in the June issue. Subscribe now. Let us know what you think about this article. Submit a letter to the editor at [email protected]. Why Didn't Artificial Intelligence Save Us From Covid-19 ? As Machines Get Smarter, How Will We Relate to Them ? Are AI-Powered Killer Robots Inevitable ? It's Called Artificial Intelligence—but What Is Intelligence ? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Topics magazine-28.06 artificial intelligence Biology Will Bedingfield Steven Levy Will Knight Will Knight Khari Johnson Will Knight Khari Johnson Niamh Rowe Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. 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1,917	2,020	"Are AI-Powered Killer Robots Inevitable? \| WIRED"	"https://www.wired.com/story/artificial-intelligence-military-robots"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons PAUL SCHARRE Business Are AI-Powered Killer Robots Inevitable? Photograph: Getty Images Save this story Save Save this story Save Application Ethics Hardware Recommendation algorithm Regulation Safety Human-computer interaction End User Government Sector Defense Source Data Geolocation Images Sensors Video Technology Machine learning Machine vision Robotics In war, speed kills. The soldier who is a split second quicker on the draw may walk away from a firefight unscathed; the ship that sinks an enemy vessel first may spare itself a volley of missiles. In cases where humans can't keep up with the pace of modern conflict, machines step in. When a rocket-propelled grenade is streaking toward an armored ground vehicle, an automated system onboard the vehicle identifies the threat, tracks it, and fires a countermeasure to intercept it, all before the crew inside is even aware. Similarly, US Navy ships equipped with the Aegis combat system can switch on Auto-Special mode, which automatically swats down incoming warheads according to carefully programmed rules. These kinds of defensive systems have been around for decades, and at least 30 countries now use them. In many ways, they're akin to the automatic braking systems in newer cars, intervening only under specific emergency conditions. But militaries, like automakers, have gradually been giving machines freer rein. In an exercise last year, the United States demonstrated how automation could be used throughout the so-called kill chain: A satellite spotted a mock enemy ship and directed a surveillance plane to fly closer to confirm the identification; the surveillance plane then passed its data to an airborne command-and-control plane, which selected a naval destroyer to carry out an attack. In this scenario, automation bought more time for officers at the end of the kill chain to make an informed decision—whether or not to fire on the enemy ship. Related Stories Artificial intelligence Tom Simonite Ethics of War Tom Simonite top algorithm Elias Groll Militaries have a compelling reason to keep humans involved in lethal decisions. For one thing, they're a bulwark against malfunctions and flawed interpretations of data; they'll make sure, before pulling the trigger, that the automated system hasn't misidentified a friendly ship or neutral vessel. Beyond that, though, even the most advanced forms of artificial intelligence cannot understand context, apply judgment, or respond to novel situations as well as a person. Humans are better suited to getting inside the mind of an enemy commander, seeing through a feint, or knowing when to maintain the element of surprise and when to attack. But machines are faster, and firing first can carry a huge advantage. Given this competitive pressure, it isn't a stretch to imagine a day when the only way to stay alive is to embrace a fully automated kill chain. If just one major power were to do this, others might feel compelled to follow suit, even against their better judgment. In 2016, then deputy secretary of defense Robert Work framed the conundrum in layperson's terms: “If our competitors go to Terminators,” he asked, “and it turns out the Terminators are able to make decisions faster, even if they're bad, how would we respond?” Terminators aren't rolling off the assembly line just yet, but each new generation of weapons seems to get us closer. And while no nation has declared its intention to build fully autonomous weapons, few have forsworn them either. The risks from warfare at machine speed are far greater than just a single errant missile. Military scholars in China have hypothesized about a “battlefield singularity,” a point at which combat moves faster than human cognition. In this state of “hyperwar,” as some American strategists have dubbed it, unintended escalations could quickly spiral out of control. The 2010 “flash crash” in the stock market offers a useful parallel: Automated trading algorithms contributed to a temporary loss of nearly a trillion dollars in a single afternoon. To prevent another such calamity, financial regulators updated the circuit breakers that halt trading when prices plummet too quickly. But how do you pull the plug on a flash war? Since the late 19th century, major military powers—whether Great Britain and Germany or the United States and the USSR—have worked together to establish regulations on all manner of modern killing machines, from exploding bullets to poison gas to nuclear weapons. Sometimes, as with anti-satellite weapons and neutron bombs, formal agreements weren't necessary; the parties simply engaged in tacit restraint. The goal, in every case, has been to mitigate the harms of war. For now, no such consensus exists with fully autonomous weapons. Nearly 30 countries support a complete ban, but none of them is a major military power or robotics developer. At the United Nations, where autonomous weapons are a subject of annual debate, China, Russia, and the United States have all stymied efforts to enact a ban. (The US and Russia have objected outright, while China in 2018 proposed a ban that would be effectively meaningless.) One of the challenging dynamics at the UN is the tug-of-war between NGOs such as the Campaign to Stop Killer Robots, whose goal is disarmament, and militaries, which won't agree to disarm unless they can verify that their adversaries will too. Autonomous weapons present some unique challenges to regulation. They can't be observed and quantified in quite the same way as, say, a 1.5-megaton nuclear warhead. Just what constitutes autonomy, and how much of it should be allowed? How do you distinguish an adversary's remotely piloted drone from one equipped with Terminator software? Unless security analysts can find satisfactory answers to these questions and China, Russia, and the US can decide on mutually agreeable limits, the march of automation will continue. And whichever way the major powers lead, the rest of the world will inevitably follow. When you buy something using the retail links in our stories, we may earn a small affiliate commission. Read more about how this works. PAUL SCHARRE (@paul_scharre) is a senior fellow at the Center for a New American Security and the author of Army of None: Autonomous Weapons and the Future of War. This article appears in the June issue. Subscribe now. Let us know what you think about this article. Submit a letter to the editor at [email protected]. Is the Brain a Useful Model for Artificial Intelligence ? Why Didn't Artificial Intelligence Save Us From Covid-19 ? As Machines Get Smarter, How Will We Relate to Them ? It's Called Artificial Intelligence—but What Is Intelligence ? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Topics magazine-28.06 war artificial intelligence Will Knight Will Knight Will Knight Peter Guest Aarian Marshall Khari Johnson Matt Laslo Khari Johnson Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. 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1,918	2,020	"Why Didn't Artificial Intelligence Save Us From Covid-19? \| WIRED"	"https://www.wired.com/story/artificial-intelligence-couldnt-save-us-from-covid-19"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Gregory Barber Business Why Didn't Artificial Intelligence Save Us From Covid-19? Photograph: Chris McGrath/Getty Images Save this story Save Save this story Save Application Recommendation algorithm Prediction Ethics Regulation Company Google End User Research Government Sector Health care Research Source Data Images Technology Machine learning Machine vision In late January, more than a week before Covid-19 had been given that name, hospitals in Wuhan, China, began testing a new method to screen for the disease, using artificial intelligence. The plan involved chest CTs—three-dimensional scans of lungs displayed in finely detailed slices. By studying thousands of such images, an algorithm would learn to decipher whether a given patient's pneumonia appeared to stem from Covid-19 or something more routine, like influenza. In the US, as the virus spread in February, the idea appeared to hold promise: With conventional tests in short supply, here was a way to get more people screened, fast. Health professionals, however, weren't so sure. Although various diagnostic algorithms have won approval from the US Food and Drug Administration—for wrist fractures, eye diseases, breast cancer—they generally spend months or years in development. They're deployed in different hospitals filled with different kinds of patients, interrogated for flaws and biases, pruned and tested again and again. Related Stories Public Health Gregory Barber Opinion Alex Engler Machine Vision Tom Simonite Was there enough data on the new virus to truly discern one pneumonia from another? What about mild cases, where the damage may be less clear? The pandemic wasn't waiting for answers, but medicine would have to. In late March, the United Nations and the World Health Organization issued a report examining the lung CT tool and a range of other AI applications in the fight against Covid-19. The politely bureaucratic assessment was that few projects had achieved “operational maturity.” The limitations were older than the crisis, but aggravated by it. Reliable AI depends on our human ability to collect data and make sense of it. The pandemic has been a case study in why that's hard to do mid-crisis. Consider the shifting advice on mask wearing and on taking ibuprofen, the doctors wrestling with who should get a ventilator and when. Our daily movements are dictated by uncertain projections of who will get infected or die, and how many more will die if we fail to self-isolate. As we sort out that evidence, AI lags a step behind us. Yet we still imagine that it possesses more foresight than we do. Take drug development. One of the flashiest AI experiments is by Google-affiliated DeepMind. The company's AlphaFold system is a champion at the art of protein modeling—predicting the shape of tiny structures that make up the virus. In the lab, divining those structures can be a months-long process; DeepMind, when it released schematics for six viral proteins in March, had done it in days. The models were approximations, the team cautioned, churned out by an experimental system. But the news left an impression: AI had joined the vaccine race. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg In the vaccine community, however, the effort elicited a shrug. “I can't see much of a role for AI right now,” says Julia Schaletzky, a veteran drug discovery researcher and head of UC Berkeley's Center for Emerging and Neglected Diseases. Plenty of well-defined protein targets have been confirmed in labs without the help of AI. It would be risky to spend precious time and grants starting from scratch, using the products of an experimental system. Technological progress is good, Schaletzky says, but it's often pushed at the expense of building on what's known and promising. She says there's potential in using AI to help find treatments. AI algorithms can complement other data-mining techniques to help us sift through reams of information we already have—to spot encouraging threads of research, for example, or older treatments that hold promise. One drug identified this way, baricitinib, is now going to clinical trials. Another hope is that AI could yield insights into how Covid-19 attacks the body. An algorithm could mine lots of patient records and determine who is more at risk of dying and who is more likely to survive, turning anecdotes whispered between doctors into treatment plans. But again, it's all a matter of data—what data we've already gathered, and whether we've organized it in a way that's useful for machines. Our health care system doesn't give up information easily to train such systems; privacy regulations and balkanized data silos will stop you even before the antiquated, error-filled health databases do. It's possible this crisis will change that. Maybe it will push us to rethink how data is stored and shared. Maybe we'll keep studying this virus even after the chaos dissipates and the attention wanes, giving us solid data—and better AI—when the next pandemic arrives. For now, though, we can't be surprised that AI hasn't saved us from this one. This article appears in the June issue. Subscribe now. Let us know what you think about this article. Submit a letter to the editor at [email protected]. Is the Brain a Useful Model for Artificial Intelligence ? As Machines Get Smarter, How Will We Relate to Them ? Are AI-Powered Killer Robots Inevitable ? It's Called Artificial Intelligence—but What Is Intelligence ? Staff Writer X Topics magazine-28.06 COVID-19 coronavirus artificial intelligence Will Knight Will Knight Khari Johnson Will Knight Khari Johnson Niamh Rowe Matt Laslo Peter Guest Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. 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1,919	2,016	"Sure, A.I. Is Powerful—But Can We Make It Accountable? \| WIRED"	"https://www.wired.com/2016/10/understanding-artificial-intelligence-decisions"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Clive Thompson Culture Sure, A.I. Is Powerful—But Can We Make It Accountable? Zohar Lazar Save this story Save Save this story Save Say you apply for home insurance and get turned down. You ask why, and the company explains its reasoning: Your neighborhood is at high risk for flooding, or your credit is dodgy. Fair enough. Now imagine you apply to a firm that uses a machine-learning system, instead of a human with an actuarial table, to predict insurance risk. After crunching your info—age, job, house location and value—the machine decides, nope, no policy for you. You ask the same question: “Why?” The Future of Humanity’s Food Supply Is in the Hands of AI AI’s Next Frontier: Machines That Understand Language Artificial Intelligence Finally Entered Our Everyday World Now things get weird. Nobody can answer, because nobody understands how these systems—neural networks modeled on the human brain—produce their results. Computer scientists “train” each one by feeding it data, and it gradually learns. But once a neural net is working well, it’s a black box. Ask its creator how it achieves a certain result and you’ll likely get a shrug. The opacity of machine learning isn’t just an academic problem. More and more places use the technology for everything from image recognition to medical diagnoses. All that decisionmaking is, by definition, unknowable—and that makes people uneasy. My friend Zeynep Tufekci, a sociologist, warns about “Moore’s law plus inscrutability.” Microsoft CEO Satya Nadella says we need “algorithmic accountability.” All that is behind the fight to make machine learning more comprehensible. This spring, the European Union passed a regulation giving its citizens what University of Oxford researcher Bryce Goodman describes as an effective “right to an explanation” for decisions made by machine-learning systems. Starting in 2018, EU citizens will be entitled to know how an institution arrived at a conclusion—even if an AI did the concluding. Jan Albrecht, an EU legislator from Germany, thinks explanations are crucial for public acceptance of artificial intelligence. “Otherwise people are afraid of it,” he says. “There needs to be someone who has control.” Explanations of what’s happening inside the black box could also help ferret out bias in the systems. If a system for approving bank loans were trained on data that had relatively few black people in it, Goodman says, it might be uncertain about black applicants—and be more likely to reject them. So sure, more clarity would be good. But is it possible ? The box is, after all, black. Early experiments have shown promise. At the machine-learning company Clarifai, founder Matt Zeiler analyzed a neural net trained to recognize images of animals and objects. By blocking out portions of pictures and seeing how the different “layers” inside the net responded, he could begin to see which parts were responsible for recognizing, say, faces. Researchers at the University of Amsterdam have pursued a similar approach. Google, which has a large stake in AI, is doing its own probing: Its hallucinogenic “deep dreaming” pictures emerged from experiments that amplified errors in machine learning to figure out how the systems worked. Of course, there’s self-interest operating here too. The more that companies grasp what’s going on inside their AIs, the more they can improve their products. The first stage of machine learning was just building these new brains. Now comes the Freudian phase: analysis. “I think we’re going to get better and better,” Zeiler says. Granted, these are still early days. The people probing the black boxes might run up against some inherent limits to human comprehension. If machine learning is powerful because it processes data in ways we can’t, it might seem like a waste of time to try to dissect it—and might even hamper its development. But the stakes for society are too high, and the challenge is frankly too fascinating. Human beings are creating a new breed of intelligence; it would be irresponsible not to try to understand it. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Contributor Topics artificial intelligence machine learning magazine-24.10 neural networks Kate Knibbs Jason Parham Vauhini Vara Virginia Heffernan Gideon Lichfield Kate Knibbs Lindsay Jones Jason Parham Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,920	2,016	"How Google is Remaking Itself as a “Machine Learning First” Company \| WIRED"	"https://www.wired.com/2016/06/how-google-is-remaking-itself-as-a-machine-learning-first-company"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Steven Levy Backchannel How Google is Remaking Itself as a “Machine Learning First” Company Save this story Save Save this story Save Carson Holgate is training to become a ninja. Not in the martial arts — she’s already done that. Holgate, 26, holds a second degree black belt in Tae Kwon Do. This time it’s algorithmic. Holgate is several weeks into a program that will inculcate her in an even more powerful practice than physical combat: machine learning, or ML. A Google engineer in the Android division, Holgate is one of 18 programmers in this year’s Machine Learning Ninja Program, which pulls talented coders from their teams to participate, Ender’s Game -style, in a regimen that teaches them the artificial intelligence techniques that will make their products smarter. Even if it makes the software they create harder to understand. Carson Holgate Jason Henry “The tagline is, Do you want to be a machine learning ninja? ” says Christine Robson, a product manager for Google’s internal machine learning efforts, who helps administer the program. “So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot.” For Holgate, who came to Google almost four years ago with a degree in computer science and math, it’s a chance to master the hottest paradigm of the software world: using learning algorithms (“learners”) and tons of data to “teach” software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by “neural nets” that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans. Google is committed to expanding that elite within its walls, with the hope of making it the norm. For engineers like Holgate, the ninja program is a chance to leap to the forefront of the effort, learning from the best of the best. “These people are building ridiculous models and have PhD’s,” she says, unable to mask the awe in her voice. She’s even gotten over the fact that she is actually in a program that calls its students “ninjas.” “At first, I cringed, but I learned to accept it,” she says. Steven Levy is Backchannel's founder and Editor in Chief. --------- Sign up to get Backchannel's weekly newsletter, and follow us on Facebook and Twitter. Considering the vast size of Google’s workforce — probably almost half of its 60,000 headcount are engineers — this is a tiny project. But the program symbolizes a cognitive shift in the company. Though machine learning has long been part of Google’s technology — and Google has been a leader in hiring experts in the field — the company circa 2016 is obsessed with it. In an earnings call late last year, CEO Sundar Pichai laid out the corporate mindset: “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.” Obviously, if Google is to build machine learning in all its products, it needs engineers who have mastery of those techniques, which represents a sharp fork from traditional coding. As Pedro Domingos, author of the popular ML manifesto The Master Algorithm , writes, “Machine learning is something new under the sun: a technology that builds itself.” Writing such systems involves identifying the right data, choosing the right algorithmic approach, and making sure you build the right conditions for success. And then (this is hard for coders) trusting the systems to do the work. “The more people who think about solving problems in this way, the better we’ll be,” says a leader in the firm’s ML effort, Jeff Dean, who is to software at Google as Tom Brady is to quarterbacking in the NFL. Today, he estimates that of Google’s 25,000 engineers, only a “few thousand” are proficient in machine learning. Maybe ten percent. He’d like that to be closer to a hundred percent. “It would be great to have every engineer have at least some amount of knowledge of machine learning,” he says. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Does he think that will happen? “We’re going to try,” he says. For years, John Giannandrea has been Google’s key promoter of machine learning, and, in a flashing neon sign of where the company is now, he recently became the head of Search. But when he arrived at the company in 2010 (as part of the company’s acquisition of MetaWeb, a vast database of people, places and things that is now integrated into Google Search as the Knowledge Graph), he didn’t have much experience with ML or neural nets. Around 2011, though, he became struck by news coming from a conference called Neural Information Processing Systems (NIPS). It seemed every year at NIPS some team or other would announce results using machine learning that blew away previous attempts at a solving a problem, be it translation, voice recognition, or vision. Something amazing was happening. “When I was first looking at it, this NIPS conference was obscure,” he says. “But this whole area across academia and industry has ballooned in the last three years. I think last year 6000 attended.” Jeff Dean Jason Henry Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg These improved neural-net algorithms along with more powerful computation from the Moore’s Law effect and an exponential increase in data drawn from the behavior of huge numbers of users at companies like Google and Facebook, began a new era of ascendant machine learning. Giannandrea joined those who believed it should be central to the company. That cohort included Dean, co-founder of the Google Brain , a neural net project originating in the company’s long-range research division Google X. (Now known simply as X.) Google’s bear-hug-level embrace of machine learning does not simply represent a shift in programming technique. It’s a serious commitment to techniques that will bestow hitherto unattainable powers to computers. The leading edge of this are “deep learning” algorithms built around sophisticated neural nets inspired by brain architecture. Google Brain is a deep learning effort, and DeepMind, the AI company Google bought for a reported $500 million in January 2014, also concentrates on that end of the spectrum. It was DeepMind that created the AlphaGo system that beat a champion of Go, shattering expectations of intelligent machine performance and sending ripples of concern among those fearful of smart machines and killer robots. While Giannandrea dismisses the “AI-is-going-to-kill us” camp as ill-informed Cassandras, he does contend that machine learning systems are going to be transformative, in everything from medical diagnoses to driving our cars. While machine learning won’t replace humans, it will change humanity. The example Giannandrea cites to demonstrate machine learning power is Google Photos, a product whose definitive feature is an uncanny — maybe even disturbing — ability to locate an image of something specified by the user. Show me pictures of border collies. “When people see that for the first time they think something different is happening because the computer is not just computing a preference for you or suggesting a video for you to watch,” says Giannandrea. “It’s actually understanding what’s in the picture.” He explains that through the learning process, the computer “knows” what a border collie looks like, and it will find pictures of it when it’s a puppy, when its old, when it’s long-haired, and when it’s been shorn. A person could do that, of course. But no human could sort through a million examples and simultaneously identify ten thousand dog breeds. But a machine learning system can. If it learns one breed, it can use the same technique to identify the other 9999 using the same technique. “That’s really what’s new here,” says Giannandrea. “For those narrow domains, you’re seeing what some people call super human performance in these learned systems.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg To be sure, machine learning concepts have long been understood at Google, whose founders are lifetime believers of the power of artificial intelligence. Machine learning is already baked into many Google products, albeit not always the more recent flavors centering around neural nets. (Earlier machine learning often relied on a more straightforward statistical approach.) In fact, over a decade ago, Google was running in-house courses to teach its engineers machine learning. In the early 2005, Peter Norvig, then in charge of search, suggested to a research scientist named David Pablo Cohn that he look into whether Google might adopt a course in the subject organized by Carnegie Mellon University. Cohn concluded that only Googlers themselves could teach such an internal course, because Google operated at such a different scale than anyone else (except maybe the Department of Defense). So he reserved a large room in Building 43 (then the headquarters of the search team) and held a two-hour class every Wednesday. Even Jeff Dean dropped in for a couple of sessions. “It was the best class in the world,” Cohn says. “They were all much better engineers than I was!” The course was so popular, in fact, that it began to get out of hand. People in the Bangalore office were staying past midnight so they could call in. After a couple of years, some Googlers helped put the lectures on short videos; the live sessions ended. Cohn believes it might have qualified as a precursor to the Massive Open Online Course (MOOC). Over the next few years there were other disparate efforts at ML training at Google, but not in an organized, coherent fashion. Cohn left Google in 2013 just before, he says, ML at Google “suddenly became this all-important thing.” That understanding hadn’t yet hit in 2012 when Giannandrea had the idea to “get a bunch of people who were doing this stuff” and put them in a single building. Google Brain, which had “graduated” from the X division, joined the party. “We uprooted a bunch of teams, put them in a building, got a nice new coffee machine,” he says. “People who previously had just been working on what we called perception — sound and speech understanding and so on — were now talking to the people who were trying to work on language.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg More and more, the machine learning efforts from those engineers began appearing in Google’s popular products. Since key machine learning domains are vision, speech, voice recognition, and translation, it’s unsurprising that ML is now a big part of Voice Search, Translate, and Photos. More striking is the effort to work machine learning into everything. Jeff Dean says that as he and his team have begun to understand ML more, they are exploiting it in more ambitious ways. “Previously, we might use machine learning in a few sub-components of a system,” he says. “Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces.” If he were to rewrite Google’s infrastructure today, says Dean, who is known as the co-creator of game-changing systems like Big Table and MapReduce , much of it would not be coded but learned. Greg Corrado, co-founder of Google Brain Jason Henry Machine learning also is enabling product features that previously would have been unimaginable. One example is Smart Repl y in Gmail, launched in November 2015. It began with a conversation between Greg Corrado, a co-founder of the Google Brain project, and a Gmail engineer named Bálint Miklós. Corrado had previously worked with the Gmail team on using ML algorithms for spam detection and classifying email, but Miklós suggested something radical. What if the team used machine learning to automatically generate replies to emails, saving mobile users the hassle of tapping out answers on those tiny keyboards? “I was actually flabbergasted because the suggestion seemed so crazy,” says Corrado. “But then I thought that with the predictive neural net technology we’d been working on, it might be possible. And once we realized there was even a chance, we had to try.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Google boosted the odds by keeping Corrado and his team in close and constant contact with the Gmail group, an approach that is increasingly common as machine learning experts fan out among product groups. “Machine learning is as much art as it is science,” says Corrado. “It’s like cooking — yes, there’s chemistry involved but to do something really interesting, you have to learn how to combine the ingredients available to you.” Traditional AI methods of language understanding depended on embedding rules of language into a system, but in this project, as with all modern machine learning, the system was fed enough data to learn on its own, just as a child would. “I didn’t learn to talk from a linguist, I learned to talk from hearing other people talk,” says Corrado. But what made Smart Reply really feasible was that success could be easily defined — the idea wasn’t to create a virtual Scarlett Johansson who would engage in flirtatious chatter, but plausible replies to real-life emails. “What success looked like is that the machine generated a candidate response that people found useful enough to use as their real response,” he says. Thus the system could be trained by noting whether or not users actually clicked on the suggested replies. When the team began testing Smart Reply, though, users noted a weird quirk: it would often suggest inappropriate romantic responses. “One of the failure modes was this really hysterical tendency for it to say, ‘I love you’ whenever it got confused,” says Corrado. “It wasn’t a software bug — it was an error in what we asked it to do.” The program had somehow learned a subtle aspect of human behavior: “If you’re cornered, saying, ‘I love you’ is a good defensive strategy.” Corrado was able to help the team tamp down the ardor. Smart Repl y, released last November, is a hit — users of the Gmail Inbox app now routinely get a choice of three potential replies to emails that they can dash off with a single touch. Often they seem uncannily on the mark. Of responses sent by mobile Inbox users, one in ten is created by the machine-learning system. “It’s still kind of surprising to me that it works,” says Corrado with a laugh. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Smart Reply is only one data point in a dense graph of instances where ML has proved effective at Google. But perhaps the ultimate turning point came when machine learning became an integral part of search, Google’s flagship product and the font of virtually all its revenues. Search has always been based on artificial intelligence to some degree. But for many years, the company’s most sacred algorithms, those that delivered what were once known as the “ten blue links” in response to a search query, were deemed too important for ML’s learning algorithms. “Because search is such a large part of the company, ranking is very, very highly evolved, and there was a lot of skepticism you could move the needle very much,” says Giannandrea. In part this was a cultural resistance — a stubborn microcosm of the general challenge of getting control-freaky master hackers to adopt the Zen-ish machine learning approach. Amit Singhal, the long-time maester of search, was himself an acolyte of Gerald Salton, a legendary computer scientist whose pioneering work in document retrieval inspired Singhal to help revise the grad-student code of Brin and Page into something that could scale in the modern web era. (This put him in the school of “retrievers.”) He teased amazing results from those 20th century methods, and was suspicious of integrating learners into the complicated system that was Google’s lifeblood. “My first two years at Google I was in search quality, trying to use machine learning to improve ranking,” says David Pablo Cohn. “It turns out that Amit’s intuition was the best in the world, and we did better by trying to hard code whatever was in Amit’s brain. We couldn’t find anything as good as his approach.” Related Stories Uncategorized Steven Levy Uncategorized Steven Levy Uncategorized Sandra Upson By early 2014, Google’s machine learning masters believed that should change. “We had a series of discussions with the ranking team,” says Dean. “We said we should at least try this and see, is there any gain to be had.” The experiment his team had in mind turned out to be central to search: how well a document in the ranking matches a query (as measured by whether the user clicks on it). “We sort of just said, let’s try to compute this extra score from the neural net and see if that’s a useful score.” It turned out the answer was yes, and the system is now part of search, known as RankBrain. It went online in April 2015. Google is characteristically fuzzy on exactly how it improves search (something to do with the long tail? Better interpretation of ambiguous requests?) but Dean says that RankBrain is “involved in every query,” and affects the actual rankings “probably not in every query but in a lot of queries.” What’s more, it’s hugely effective. Of the hundreds of “signals” Google search uses when it calculates its rankings (a signal might be the user’s geographical location, or whether the headline on a page matches the text in the query), RankBrain is now rated as the third most useful. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “It was significant to the company that we were successful in making search better with machine learning,” says Giannandrea. “That caused a lot of people to pay attention.” Pedro Domingos, the University of Washington professor who wrote The Master Algorithm puts it a different way: “There was always this battle between the retrievers and the machine learning people,” he says. “The machine learners have finally won the battle.” Google’s new challenge is shifting its engineering workforce so everyone is familiar, if not adept, at machine learning. It’s a goal pursued now by many other companies, notably Facebook, which is just as gaga about ML and deep learning as Google is. The competition to hire recent graduates in the field is fierce, and Google tries hard to maintain its early lead; for years, the joke in academia was that Google hires top students even when it doesn’t need them, just to deny them to the competition. (The joke misses the point that Google does need them.) “My students, no matter who, always get an offer from Google.” says Domingos. And things are getting tougher: just last week, Google announced it will open a brand new machine-learning research lab in Zurich, with a whole lot of workstations to fill. But since academic programs are not yet producing ML experts in huge numbers, retraining workers is a necessity. And that isn’t always easy, especially at a company like Google, with many world-class engineers who have spent a lifetime achieving wizardry through traditional coding. Machine learning requires a different mindset. People who are master coders often become that way because they thrive on the total control that one can have by programming a system. Machine learning also requires a grasp of certain kinds of math and statistics, which many coders, even gonzo hackers who can zip off tight programs of brobdingnagian length, never bothered to learn. Christine Robson Jason Henry Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg It also requires a degree of patience. “The machine learning model is not a static piece of code — you're constantly feeding it data,” says Robson. “We are constantly updating the models and learning, adding more data and tweaking how we're going to make predictions. It feels like a living, breathing thing. It’s a different kind of engineering.” “It’s a discipline really of doing experimentation with the different algorithms, or about which sets of training data work really well for your use case,” says Giannandrea, who despite his new role as search czar still considers evangelizing machine learning internally as part of his job. “The computer science part doesn’t go away. But there is more of a focus on mathematics and statistics and less of a focus on writing half a million lines of code.” As far as Google is concerned, this hurdle can be leapt over by smart re-training. “At the end of the day the mathematics used in these models is not that sophisticated,” says Dean. “It’s achievable for most software engineers we would hire at Google.” To further aid its growing cadre of machine learning experts, Google has built a powerful set of tools to help engineers make the right choices of the models they use to train their algorithms and to expedite the process of training and refining. The most powerful of those is TensorFlow , a system that expedites the process of constructing neural nets. Built out of the Google Brain project, and co-invented by Dean and his colleague Rajat Monga, TensorFlow helped democratize machine learning by standardizing the often tedious and esoteric details involved in building a system — especially since Google made it available to the public in November 2015. While Google takes pains to couch the move as an altruistic boon to the community, it also acknowledges that a new generation of programmers familiar with its in-house machine learning tools is a pretty good thing for Google recruiting. (Skeptics have noted that Google’s open-sourcing TensorFlow is a catch-up move with Facebook, which publicly released deep-learning modules for an earlier ML system, Torch, in January 2015.) Still, TensorFlow’s features, along with the Google imprimatur, have rapidly made it a favorite in ML programming circles. According to Giannandrea, when Google offered its first online TensorFlow course, 75,000 people signed up. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Google still saves plenty of goodies for its own programmers. Internally, the company has a probably unparalleled tool chest of ML prosthetics, not the least of which is an innovation it has been using for years but announced only recently — the Tensor Processing Unit. This is a microprocessor chip optimized for the specific quirks of running machine language programs, similar to the way as Graphics Processing Units are designed with the single purpose of speeding the calculations that throw pixels on a display screen. Many thousands (only God and Larry Page probably know how many) are inside servers in the company’s huge data centers. By super-powering its neural net operations, TPU’s give Google a tremendous advantage. “We could not have done RankBrain without it,” says Dean. But since Google’s biggest need is people to design and refine these systems, just as the company is working feverishly to refine its software-training tools, it’s madly honing its experiments in training machine-learning engineers. They range from small to large. The latter category includes quick-and-dirty two-day “Machine Learning Crash Course with TensorFlow,” with slides and exercises. Google hopes this is a first taste, and the engineers will subsequently seek out resources to learn more. “We have thousands of people signed up for the next offering of this one course,” says Dean. Other, smaller efforts draw outsiders into Google’s machine learning maw. Earlier this spring, Google began the Brain Residency program, a program to bring in promising outsiders for a year of intense training from within the Google Brain group. “We’re calling it a jump start in your Deep Learning career,” says Robson, who helps administer the program. Though it’s possible that some of the 27 machine-learning-nauts from different disciplines in the initial program might wind up sticking around Google, the stated purpose of the class is to dispatch them back in the wild, using their superpowers to spread Google’s version of machine learning throughout the data-sphere. So, in a sense, what Carson Holgate learns in her ninja program is central to how Google plans to maintain its dominance as an AI-focused company in a world where machine learning is taking center stage. Her program began with a four-week boot camp where the product leads of Google’s most advanced AI projects drilled them on the fine points of baking machine learning into projects. “We throw the ninjas into a conference room and Greg Corrado is there at the white board, explaining LSTM [“Long Short Term Memory,” a technique that makes powerful neural nets], gesturing wildly, showing how this really works, what the math is, how to use it in production,” says Robson. “We basically just do this with every technique we have and every tool in our toolbox for the first four weeks to give them a really immersive dive.” Holgate has survived boot camp and now is using machine learning tools to build a communications feature in Android that will help Googlers communicate with each other. She’s tuning hyperparameters. She’s cleansing her input data. She’s stripping out the stop words. But there’s no way she’s turning back, because she knows that these artificial intelligence techniques are the present and the future of Google, maybe of all technology. Maybe of everything. “Machine learning,” she says, “is huge here.” Creative Art Direction by Redindhi Editor at Large X Topics Backchannel Google machine learning artificial intelligence Brandi Collins-Dexter Steven Levy Andy Greenberg Angela Watercutter Lauren Smiley Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. 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1,921	2,017	"Uber Hires an AI Superstar in the Quest to Rehab Its Future \| WIRED"	"https://www.wired.com/2017/05/uber-hires-ai-superstar-quest-rehab-future"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Cade Metz Business Uber Hires an AI Superstar in the Quest to Rehab Its Future Uber Save this story Save Save this story Save Uber is hiring Raquel Urtasun, a prominent artificial intelligence researcher at the University of Toronto, as the ride-hailing company aims to build a lab for driverless car research in the Canadian city, a hotbed for AI talent. Urtasun ---an associate professor at the university who specializes in the computer vision software that allows driverless cars to view the world around them---will oversee the new venture. "We hope to draw from the region’s impressive talent pool as we grow, helping the dozens of researchers we plan to hire stay connected to the Toronto-Waterloo Corridor," Travis Kalanick, Uber's embattled CEO, wrote in a blog post published this morning. The move resonates on multiple levels, given the ongoing legal attack against Uber's existing computer vision technology by Waymo---the driverless car company that grew out of Google---and the widespread controversy over Uber's allegedly misogynistic internal culture. Urtasun could help the company forge another much-needed path to the kind of AI that driverless cars will require. She's also a high-profile female hire for a company desperate to change its image. Judge in Waymo Dispute Lets Uber’s Self-Driving Program Live—for Now Uber Really Seriously Promises Flying Cars by 2020 Uber Didn’t Track Users Who Deleted the App, But It Still Broke the Rules Urtasun says she's well aware of the controversy over Uber's culture but expresses confidence that change is underway. "I had a lengthy conversation with Travis," she says. "I am really convinced he is taking all the necessary steps." Now, because of her importance to the future of the company, her hiring becomes a test of whether those steps will be successful. "People will view her as a canary in the coal mine," says Chris Nicholson, CEO of the San Francisco-based AI company Skymind. But this is ultimately about far more than just the future of Uber. Like so much of the tech industry, the field of artificial intelligence is dominated by men. And that is particularly worrying, given that so much of today's AI research involves using data to teach machines ways of mimicking human perception and behavior. The field---and the world it will impact---need a diverse array of data and people shaping these technologies. "The question is: Can we build systems that are fair, that aren't biased?" Urtasun says. At Uber, this begins with autonomous vehicles. Though Uber rose to prominence offering a smartphone app for hailing rides, it has rapidly embraced the idea of driverless cars over the past few years, building a research-and-development hub dedicated to the technology near Carnegie Mellon University in Pittsburgh and purchasing the San Francisco autonomous vehicle company Otto for an estimated $680 million. But its plans were thrown into doubt when Waymo sued the company, accusing Anthony Levandowski, an Otto founder and former Googler, of stealing trade secrets related to the lidar sensors that help driverless cars view the world around them. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "Uber views this arena---driverless cars---as a strategic fight that just they just can't afford to lose," says Oren Etzioni, a former AI researcher at the University of Washington and now the CEO of the Allen Institute for AI. "If Uber loses the fight, it is a radical shift in the value of the company." Meanwhile, many big-name employees have left the company as the controversies grew. These include Raffi Krikorian, a director in the self-driving division, and Gary Marcus, who joined Uber after the company acquired his AI startup, Geometric Intelligence. In hiring Urtasun, Uber is very much trying to recover from the damage already wrought by the Waymo lawsuit, though the court battle is just beginning. "One bet is now heavily litigated," Etzioni says. "It makes sense to lay down another bet." The researchers who came to Uber through the acquisition of Geometric Intelligence will continue to serve as Uber's central AI lab, and the company will continue to run parts of its Advanced Technologies Group---essentially its driverless cars lab---in Pittsburgh and the San Francisco Bay Area. Urtasun will create a third arm of the group in Toronto. The University of Toronto is one of the prime centers for researchers who specialize in deep neural networks, a form of AI that has rapidly reinvented computer vision and so many other online services in recent years. Many of the largest tech companies, particularly Google, have drawn talent from the city, and they are intent on keeping the pipeline running. One of Google's most important researchers, Geoff Hinton, recently helped create a kind of AI incubator in the area with financial backing from the search giant. Urtasun was one of the organization's cofounders, and now Uber is backing it too, hoping to draw talent of its own from the area. In short, Uber is doing so many of the right things in hiring Urtasun. Given the breadth of the company's troubles, that may or may not be enough. Senior Writer X Topics artificial intelligence Uber Women in tech Will Knight Khari Johnson Amit Katwala Kari McMahon David Gilbert Andy Greenberg David Gilbert Joel Khalili Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. 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1,922	2,016	"2016: The Year That Deep Learning Took Over the Internet \| WIRED"	"https://www.wired.com/2016/12/2016-year-deep-learning-took-internet"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Cade Metz Business 2016: The Year That Deep Learning Took Over the Internet Getty Images Save this story Save Save this story Save On the west coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth---part of an effort to prevent the extinction of these endangered marine mammals. The trouble is that Hodgson and her team don't have the time needed to examine all those aerial photos. There are too many of them---about 45,000---and spotting the dugongs is far too difficult for the untrained eye. So she's giving the job to a deep neural network. Deep learning is remaking Google, Facebook, Microsoft, and Amazon. Neural networks are the machine learning models that identify faces in the photos posted to your Facebook news feed. They also recognize the questions you ask your Android phone, and they help run the Google search engine. Modeled loosely on the network of neurons in the human brain, these sweeping mathematical models learn all these things by analyzing vast troves of digital data. Now, Hodgson, a marine biologist at Murdoch University in Perth , is using this same technique to find dugongs in thousands of photos of open water, running her neural network on the same open-source software, TensorFlow, that underpins the machine learning services inside Google. As Hodgson explains, detecting these sea cows is a task that requires a particular kind of pinpoint accuracy, mainly because these animals feed below the surface of the ocean. "They can look like whitecaps or glare on the water," she says. But that neural network can now identify about 80 percent of dugongs spread across the bay. The project is still in the early stages, but it hints at the widespread impact of deep learning over past year. In 2016, this very old but newly powerful technology helped a Google machine beat one of the world's top players at the ancient game of Go —a feat that didn't seem possible just a few months before. But that was merely the most conspicuous example. As the year comes to a close, deep learning isn't a party trick. It's not niche research. It's remaking companies like Google, Facebook, Microsoft, and Amazon from the inside out , and it's rapidly spreading to the rest of the world, thanks in large part to the open source software and cloud computing services offered by these giants of the internet. In previous years, neural nets reinvented image recognition through apps like Google Photos, and they took speech recognition to new levels via digital assistants like Google Now and Microsoft Cortana. This year, they delivered the big leap in machine translation, the ability to automatically translate speech from one language to another. In September, Google rolled out a new service it calls Google Neural Machine Translation , which operates entirely through neural networks. According to the company, this new engine has reduced error rates between 55 and 85 percent when translating between certain languages. Google, Facebook, and Microsoft Are Remaking Themselves Around AI Giant Corporations Are Hoarding the World’s AI Talent Intel Looks to a New Chip to Power the Coming Age of AI Google trains these neural networks by feeding them massive collections of existing translations. Some of this training data is flawed, including lower quality translations from previous versions of the Google Translate app. But it also includes translations from human experts, and this buoys the quality of the training data as a whole. That ability to overcome imperfection is part of deep learning's apparent magic: given enough data, even if some is flawed, it can train to a level well beyond those flaws. Mike Schuster, a lead engineer on Google's service, is happy to admit that his creation is far from perfect. But it still represents a breakthrough. Because the service runs entirely on deep learning, it's easier for Google to continue improving the service. It can concentrate on refining the system as a whole, rather than juggling the many small parts that characterized machine translation services in the past. Meanwhile, Microsoft is moving in the same direction. This month, it released a version of its Microsoft Translator app that can drive instant conversations between people speaking as many as nine different languages. This new system also runs almost entirely on neural nets, says Microsoft vice president Harry Shum, who oversees the company's AI and research group. That's important, because it means Microsoft's machine translation is likely to improve more quickly as well. In 2016, deep learning also worked its way into chatbots, most notably the new Google Allo. Released this fall, Allo will analyze the texts and photos you receive and instantly suggest potential replies. It's based on an earlier Google technology called Smart Reply that does much the same with email messages. The technology works remarkably well, in large part because it respects the limitations of today's machine learning techniques. The suggested replies are wonderfully brief, and the app always suggests more than one, because, well, today's AI doesn't always get things right. Inside Allo, neural nets also help respond to the questions you ask of the Google search engine. They help the company's search assistant understand what you're asking , and they help formulate an answer. According to Google research product manager David Orr, the app's ability to zero in on an answer wouldn't be possible without deep learning. "You need to use neural networks---or at least that is the only way we have found to do it,” he says. “We have to use all of the most advanced technology we have.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg What neural nets can't do is actually carry on a real conversation. That sort of chatbot is still a long way off, whatever tech CEOs have promised from their keynote stages. But researchers at Google, Facebook, and elsewhere are exploring deep learning techniques that help reach that lofty goal. The promise is that these efforts will provide the same sort of progress we've seen with speech recognition, image recognition, and machine translation. Conversation is the next frontier. This summer, after building an AI that cracked the game of Go, Demis Hassabis and his Google DeepMind lab revealed they had also built an AI that helps operate Google's worldwide network of computer data centers. Using a technique called deep reinforcement learning, which underpins both their Go-playing machine and earlier DeepMind services that learned to master old Atari games , this AI decides when to turn on cooling fans inside the thousands of computer servers that fill these data centers, when to open the data center windows for additional cooling, and when to fall back on expensive air conditioners. All told, it controls over 120 functions inside each data center As Bloomberg reported , this AI is so effective, it saves Google hundreds of millions of dollars. In other words, it pays for the cost of acquiring DeepMind, which Google bought for about $650 million in 2014. Now, Deepmind plans on installing additional sensors in these computing facilities, so it can collect additional data and train this AI to even higher levels. As they push this technology into their own products as services, the giants of the internet are also pushing it into the hands of others. At the end of 2015, Google open sourced TensorFlow, and over the past year, this once-proprietary software spread well beyond the company's walls, all the way to people like Amanda Hodgson. At the same time, Google, Microsoft, and Amazon began offering their deep learning tech via cloud computing services that any coder or company can use to build their own apps. Artificial intelligence-as-a-service may wind up as the biggest business for all three of these online giants. As AI evolves, the role of the computer scientist is changing. Over the last twelve months, this burgeoning market spurred another AI talent grab. Google hired Stanford professor Fei-Fei Li , one of the biggest names in the world of AI research, to oversee a new cloud computing group dedicated to AI, and Amazon nabbed Carnegie Mellon professor Alex Smolna to play much the same role inside its cloud empire. The big players are grabbing the world's top AI talent as quickly as they can, leaving little for others. The good news is that this talent is working to share at least some of the resulting tech they develop with anyone who wants it. As AI evolves, the role of the computer scientist is changing. Sure, the world still needs people who can code software. But increasingly, it also needs people who can train neural networks, a very different skill that's more about coaxing a result from the data than building something on your own. Companies like Google and Facebook are not only hiring a new kind of talent, but also reeducating their existing employees for this new future—a future where AI will come to define technology in the lives of just about everyone. Senior Writer X Topics Amazon artificial intelligence deep learning Enterprise Facebook Google Microsoft Susan D'Agostino Christopher Beam Will Knight Will Knight Will Knight Niamh Rowe Steven Levy Reece Rogers Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. 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1,923	2,016	"Self-Driving Cars Will Teach Themselves to Save Lives—But Also Take Them \| WIRED"	"https://www.wired.com/2016/06/self-driving-cars-will-power-kill-wont-conscience"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Cade Metz Business Self-Driving Cars Will Teach Themselves to Save Lives—But Also Take Them Google/WIRED Save this story Save Save this story Save If you follow the ongoing creation of self-driving cars, then you probably know about the classic thought experiment called the Trolley Problem. A trolley is barreling toward five people tied to the tracks ahead. You can switch the trolly to another track---where only one person is tied down. What do you do? Or, more to the point, what does a self-driving car do? Even the people building the cars aren't sure. In fact, this conundrum is far more complex than even the pundits realize. Now, more than ever, machines can learn on their own. They've learned to recognize faces in photos and the words people speak. They've learned to choose links for Google's search engine. They've learned to play games that even artificial intelligence researchers thought they couldn't crack. In some cases, as these machines learn, they're exceeding the talents of humans. And now, they're learning to drive. What the AI Behind AlphaGo Can Teach Us About Being Human Soon We Won’t Program Computers. We’ll Train Them Like Dogs Let’s Use Self-Driving Cars to Fix America’s Busted Infrastructure So many companies and researchers are moving towards autonomous vehicles that will make decisions using deep neural networks and other forms of machine learning. These cars will learn ---to identify objects, recognize situations, and respond---by analyzing vast amounts of data, including what other cars have experienced in the past. So the question is, who solves the Trolley Problem? If engineers set the rules, they're making ethical decisions for drivers. But if a car learns on its own, it becomes its own ethical agent. It decides who to kill. "I believe that the trajectory that we're on is for the technology to implicitly make the decisions. And I'm not sure that's the best thing," says Oren Etzioni, a computer scientist at the University of Washington and the CEO of the Allen Institute for Artificial Intelligence. "We don't want technology to play God." But nobody wants engineers to play God, either. A self-learning system is quite different from a programmed system. AlphaGo, the Google AI that beat a grandmaster at Go, one of the most complex games ever created by humans, learned to play the game largely on its own , after analyzing tens of millions of moves from human players and playing countless games against itself. In fact, AlphaGo learned so well that the researchers who built it---many of them accomplished Go players---couldn't always follow the logic of its play. In many ways, this is an exhilarating phenomenon. In exceeding human talent, AlphaGo also had a way of pushing human talent to new heights. But when you bring a system like AlphaGo outside the confines of a game and put it into the real world---say, inside a car---this also means it's ethically separated from humans. Even the most advanced AI doesn't come equipped with a conscience. Self-learning cars won't see the moral dimension of these moral dilemmas. They'll just see a need to act. "We need to figure out a way to solve that," Etzioni says. "We haven't yet." Yes, the people who design these vehicles could coax them to respond in certain ways by controlling the data they learn from. But pushing an ethical sensibility into a self-driving car's AI is a tricky thing. Nobody completely understands how neural networks work, which means people can't always push them in a precise direction. But perhaps more importantly, even if people could push them towards a conscience, what conscience would those programmers choose? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "With Go or chess or Space Invaders, the goal is to win, and we know what winning looks like," says Lin. "But in ethical decision-making, there is no clear goal. That's the whole trick. Is the goal to save as many lives as possible? Is the goal to not have the responsibility for killing? There is a conflict in the first principles." To get around the fraught ambiguity of machines making ethical decisions, engineers could certainly hard-code the rules. When big moral dilemmas come up---or even small ones---the self-driving car would just shift to doing exactly what the software says. But then the ethics would lie in the hands of the engineers who wrote the software. It might seem like that'd be the same thing as when a human driver makes a decision on the road. But it isn't. Human drivers operate on instinct. They're not making calculated moral decisions. They respond as best as they can. And society has pretty much accepted that (manslaughter charges for car crashes notwithstanding). But if the moral philosophies are pre-programmed by people at Google, that's another matter. The programmers would have to think about the ethics ahead of time. "One has forethought---and is a deliberate decision. The other is not," says Patrick Lin, a philosopher at Cal Poly San Luis Obispo and a legal scholar at Stanford University. "Even if a machine makes the exact same decision as a human being, I think we'll see a legal challenge." Plus, the whole point of the Trolley Problem is that it's really, really hard to answer. If you're a Utilitarian , you save the five people at the expense of the one. But as the boy who has just been run over by the train explains in Tom Stoppard's Darkside ---a radio play that explores the Trolley Problem, moral philosophy, and the music of Pink Floyd---the answer isn't so obvious. "Being a person is respect," the boy says, pointing out that the philosopher Immanuel Kant wouldn't have switched the train to the second track. "Humanness is not like something there can be different amounts of. It's maxed out from the start. Total respect. Every time." Five lives don't outweigh one. Self-driving cars will make the roads safer. They will make fewer errors than humans. That might present a way forward---if people see that cars are better at driving than people, maybe people will start to trust the cars' ethics. "If the machine is better than humans at avoiding bad things, they will accept it," says Yann LeCun, head of AI research at Facebook, "regardless of whether there are special corner cases." A "corner case" would be an outlier problem---like the one with the trolley. What if the self-driving car must choose between killing you and killing me? But drivers probably aren't going to buy a car that will sacrifice the driver in the name of public safety. "No one wants a car that looks after the greater good," Lin says. "They want a car that looks after them." The only certainty, says Lin, is that the companies making these machines are taking a huge risk. "They're replacing the human and all the human mistakes a human driver can make, and they're absorbing this huge range of responsibility." What does Google, the company that built the Go-playing AI and is farthest along with self-driving cars, think of all this? Company representatives declined to say. In fact, such companies fear they may run into trouble even if the world realizes they're even considering these big moral issues. And if they aren't considering the problems, they're going to be even tougher to solve. Senior Writer X Topics AlphaGo artificial intelligence Enterprise ethics Self-Driving Cars Will Knight Khari Johnson Will Knight Will Knight Steven Levy Will Knight Steven Levy Aarian Marshall Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,924	2,016	"Obsessing Over AI Is the Wrong Way to Think About the Future \| WIRED"	"https://www.wired.com/2016/01/forget-ai-the-human-friendly-future-of-computing-is-already-here"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Anant Jhingran Business Obsessing Over AI Is the Wrong Way to Think About the Future Getty Images Save this story Save Save this story Save For many of us, the concept of artificial intelligence conjures up visions of a machine-dominated world, where humans are servants to the devices they created. That’s a frightening image, inspired more by Hollywood and science fiction writers than technologists and the academic community. The truth is less sensational but far more meaningful. We’re actually nowhere near the self-sustaining robots Isaac Asimov imagined in I, Robot. What we have instead is intelligence amplification (IA), a field with exponentially more potential to change the world in the immediate future. Anant Jhingran is Chief Technology Officer of Apigee , developer of an intelligent API platform for digital business. He is the former VP and CTO of IBM's Information Management Division and one of the early technologists behind IBM’s Watson computer. The distinction between AI and IA is as simple as it is significant. AI makes machines autonomous and detached from humans; IA, in on the other hand, puts humans in control and leverages computing power to amplify our capabilities. For a real-world example of IA, look no further than IBM’s Watson, an intelligence amplification machine that is often mistaken for AI. The feedback loop created by exposing intelligence to humans through APIs enables Watson machine to learn and improve the information it provides. The machine presents that information to humans and then learns from their decisions. Like much of IA, Watson becomes smarter by amplifying our own intelligence. While humans have used tools to bolster their productivity for centuries, the proliferation of application programming interfaces (APIs)—the mortar connecting the bricks of our digital world—in recent years has enabled greater access to valuable information in real time. The combination of intelligent computers, intelligent software, and APIs has profound implications for our everyday lives. Doctors, for example, stand to benefit tremendously from IA in their interactions with patients. Say you have a doctor at the Mayo Clinic making a diagnosis. The patient is relying on the doctor’s expertise—but the publication of new medical research far outpaces the doctor’s ability to consume and analyze it. That’s where IA comes in. Rather than depending on his or her finite body of knowledge, the doctor can utilize supercomputers capable of surveying vast amounts of information quickly to present decisions the doctor might not have thought of or known about. Visions of the future have distracted us from what’s possible today. Meanwhile, present-day robots can hardly stay upright. This isn’t to say artificial intelligence doesn’t have a significant role to play in the evolution of intelligent computers and they way we interact with them. Researchers at MIT, the University of Toronto, and elsewhere have advanced AI’s value in performing “soft intelligence” tasks like facial identification and pattern recognition —activities that ultimately improve judgment across the entire system. However, when it comes to “hard intelligence” activities like driving a car, AI still has a lot of learning to do. Visions of the future have distracted us from what’s possible today. While Google experiments with self-driving cars that can be derailed with a simple laser pointer , automakers around the globe have already begun introducing IA-enhanced cars that can improve safety by assisting drivers with duties like highway driving on long-distance road trips. Tesla, Volvo, and Audi have or will soon introduce “autopilot” functionality on their vehicles. Though it’s still unclear when autonomous vehicles will become affordable for most Americans — keeping them in a world of moonshots for now — IA-integrated cars are something we can advance, utilize, and benefit from today. Of course, technology will always need moonshot ideas —they're what makes humans great. But focusing too heavily on fully-formed artificial intelligence misses the great strides we’re making here and now with intelligence amplification that’s actually changing lives. The future of machine collaboration we’ve fantasized about is already here, and it's not what we've been taught to fear. Our machines really are here to serve us—all we have to do is embrace them. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Topics artificial intelligence IBM Wired Opinion Will Knight Khari Johnson Will Knight Will Knight Aarian Marshall Steven Levy Will Knight Will Knight Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,925	2,015	"Teaching Machines to Understand Us \| MIT Technology Review"	"https://www.technologyreview.com/s/540001/teaching-machines-to-understand-us"	"Featured Topics Newsletters Events Podcasts Featured Topics Newsletters Events Podcasts Teaching Machines to Understand Us By Tom Simonite archive page The first time Yann LeCun revolutionized artificial intelligence, it was a false dawn. It was 1995, and for almost a decade, the young Frenchman had been dedicated to what many computer scientists considered a bad idea: that crudely mimicking certain features of the brain was the best way to bring about intelligent machines. But LeCun had shown that this approach could produce something strikingly smart—and useful. Working at Bell Labs, he made software that roughly simulated neurons and learned to read handwritten text by looking at many different examples. Bell Labs’ corporate parent, AT&T, used it to sell the first machines capable of reading the handwriting on checks and written forms. To LeCun and a few fellow believers in artificial neural networks, it seemed to mark the beginning of an era in which machines could learn many other skills previously limited to humans. It wasn’t. “This whole project kind of disappeared on the day of its biggest success,” says LeCun. On the same day he celebrated the launch of bank machines that could read thousands of checks per hour, AT&T announced it was splitting into three companies dedicated to different markets in communications and computing. LeCun became head of research at a slimmer AT&T and was directed to work on other things; in 2002 he would leave AT&T, soon to become a professor at New York University. Meanwhile, researchers elsewhere found that they could not apply LeCun’s breakthrough to other computing problems. The brain-inspired approach to AI went back to being a fringe interest. LeCun, now a stocky 55-year-old with a ready smile and a sideways sweep of dark hair touched with gray, never stopped pursuing that fringe interest. And remarkably, the rest of the world has come around. The ideas that he and a few others nurtured in the face of over two decades of apathy and sometimes outright rejection have in the past few years produced striking results in areas like face and speech recognition. Deep learning, as the field is now known, has become a new battleground between Google and other leading technology companies that are racing to use it in consumer services. One such company is Facebook, which hired LeCun from NYU in December 2013 and put him in charge of a new artificial–intelligence research group, FAIR, that today has 50 researchers but will grow to 100. LeCun’s lab is Facebook’s first significant investment in fundamental research, and it could be crucial to the company’s attempts to become more than just a virtual social venue. It might also reshape our expectations of what machines can do. Facebook and other companies, including Google, IBM, and Microsoft, have moved quickly to get into this area in the past few years because deep learning is far better than previous AI techniques at getting computers to pick up skills that challenge machines, like understanding photos. Those more established techniques require human experts to laboriously program certain abilities, such as how to detect lines and corners in images. Deep-learning software figures out how to make sense of data for itself, without any such programming. Some systems can now recognize images or faces about as accurately as humans. Now LeCun is aiming for something much more powerful. He wants to deliver software with the language skills and common sense needed for basic conversation. Instead of having to communicate with machines by clicking buttons or entering carefully chosen search terms, we could just tell them what we want as if we were talking to another person. “Our relationship with the digital world will completely change due to intelligent agents you can interact with,” he predicts. He thinks deep learning can produce software that understands our sentences and can respond with appropriate answers, clarifying questions, or suggestions of its own. Agents that answer factual questions or book restaurants for us are one obvious—if not exactly world-changing—application. It’s also easy to see how such software might lead to more stimulating video-game characters or improve online learning. More provocatively, LeCun says systems that grasp ordinary language could get to know us well enough to understand what’s good for us. “Systems like this should be able to understand not just what people would be entertained by but what they need to see regardless of whether they will enjoy it,” he says. Such feats aren’t possible using the techniques behind the search engines, spam filters, and virtual assistants that try to understand us today. They often ignore the order of words and get by with statistical tricks like matching and counting keywords. Apple’s Siri, for example, tries to fit what you say into a small number of categories that trigger scripted responses. “They don’t really understand the text,” says LeCun. “It’s amazing that it works at all.” Meanwhile, systems that seem to have mastered complex language tasks, such as IBM’s Jeopardy! winner Watson, do it by being super-specialized to a particular format. “It’s cute as a demonstration, but not work that would really translate to any other situation,” he says. In contrast, deep-learning software may be able to make sense of language more the way humans do. Researchers at Facebook, Google, and elsewhere are developing software that has shown progress toward understanding what words mean. LeCun’s team has a system capable of reading simple stories and answering questions about them, drawing on faculties like logical deduction and a rudimentary understanding of time. However, as LeCun knows firsthand, artificial intelligence is notorious for blips of progress that stoke predictions of big leaps forward but ultimately change very little. Creating software that can handle the dazzling complexities of language is a bigger challenge than training it to recognize objects in pictures. Deep learning’s usefulness for speech recognition and image detection is beyond doubt , but it’s still just a guess that it will master language and transform our lives more radically. We don’t yet know for sure whether deep learning is a blip that will turn out to be something much bigger. Deep history The roots of deep learning reach back further than LeCun’s time at Bell Labs. He and a few others who pioneered the technique were actually resuscitating a long-dead idea in artificial intelligence. When the field got started, in the 1950s, biologists were just beginning to develop simple mathematical theories of how intelligence and learning emerge from signals passing between neurons in the brain. The core idea—still current today—was that the links between neurons are strengthened if those cells communicate frequently. The fusillade of neural activity triggered by a new experience adjusts the brain’s connections so it can understand it better the second time around. In 1956, the psychologist Frank Rosenblatt used those theories to invent a way of making simple simulations of neurons in software and hardware. The New York Times announced his work with the headline “ Electronic ‘Brain’ Teaches Itself. ” Rosenblatt’s perceptron, as he called his design, could learn how to sort simple images into categories—for instance, triangles and squares. Rosenblatt usually implemented his ideas on giant machines thickly tangled with wires, but they established the basic principles at work in artificial neural networks today. Deep learning is good at taking dictation and recognizing images. But can it master human language? One computer he built had eight simulated neurons, made from motors and dials connected to 400 light detectors. Each of the neurons received a share of the signals from the light detectors, combined them, and, depending on what they added up to, spit out either a 1 or a 0. Together those digits amounted to the perceptron’s “description” of what it saw. Initially the results were garbage. But Rosenblatt used a method called supervised learning to train a perceptron to generate results that correctly distinguished different shapes. He would show the perceptron an image along with the correct answer. Then the machine would tweak how much attention each neuron paid to its incoming signals, shifting those “weights” toward settings that would produce the right answer. After many examples, those tweaks endowed the computer with enough smarts to correctly categorize images it had never seen before. Today’s deep-learning networks use sophisticated algorithms and have millions of simulated neurons, with billions of connections between them. But they are trained in the same way. Rosenblatt predicted that perceptrons would soon be capable of feats like greeting people by name, and his idea became a linchpin of the nascent field of artificial intelligence. Work focused on making perceptrons with more complex networks, arranged into a hierarchy of multiple learning layers. Passing images or other data successively through the layers would allow a perceptron to tackle more complex problems. Unfortunately, Rosenblatt’s learning algorithm didn’t work on multiple layers. In 1969 the AI pioneer Marvin Minsky, who had gone to high school with Rosenblatt, published a book-length critique of perceptrons that killed interest in neural networks at a stroke. Minsky claimed that getting more layers working wouldn’t make perceptrons powerful enough to be useful. Artificial–intelligence researchers abandoned the idea of making software that learned. Instead, they turned to using logic to craft working facets of intelligence—such as an aptitude for chess. Neural networks were shoved to the margins of computer science. Nonetheless, LeCun was mesmerized when he read about perceptrons as an engineering student in Paris in the early 1980s. “I was amazed that this was working and wondering why people abandoned it,” he says. He spent days at a research library near Versailles, hunting for papers published before perceptrons went extinct. Then he discovered that a small group of researchers in the United States were covertly working on neural networks again. “This was a very underground movement,” he says. In papers carefully purged of words like “neural” and “learning” to avoid rejection by reviewers, they were working on something very much like Rosenblatt’s old problem of how to train neural networks with multiple layers. LeCun joined the underground after he met its central figures in 1985, including a wry Brit named Geoff Hinton, who now works at Google and the University of Toronto. They immediately became friends, mutual admirers—and the nucleus of a small community that revived the idea of neural networking. They were sustained by a belief that using a core mechanism seen in natural intelligence was the only way to build artificial intelligence. “The only method that we knew worked was a brain, so in the long run it had to be that systems something like that could be made to work,” says Hinton. LeCun’s success at Bell Labs came about after he, Hinton, and others perfected a learning algorithm for neural networks with multiple layers. It was known as backpropagation, and it sparked a rush of interest from psychologists and computer scientists. But after LeCun’s check-reading project ended, backpropagation proved tricky to adapt to other problems, and a new way to train software to sort data was invented by a Bell Labs researcher down the hall from LeCun. It didn’t involve simulated neurons and was seen as mathematically more elegant. Very quickly it became a cornerstone of Internet companies such as Google, Amazon, and LinkedIn, which use it to train systems that block spam or suggest things for you to buy. After LeCun got to NYU in 2003, he, Hinton, and a third collaborator, University of Montreal professor Yoshua Bengio, formed what LeCun calls “the deep-learning conspiracy.” To prove that neural networks would be useful, they quietly developed ways to make them bigger, train them with larger data sets, and run them on more powerful computers. LeCun’s handwriting recognition system had had five layers of neurons, but now they could have 10 or many more. Around 2010, what was now dubbed deep learning started to beat established techniques on real-world tasks like sorting images. Microsoft, Google, and IBM added it to speech recognition systems. But neural networks were still alien to most researchers and not considered widely useful. In early 2012 LeCun wrote a fiery letter—initially published anonymously —after a paper claiming to have set a new record on a standard vision task was rejected by a leading conference. He accused the reviewers of being “clueless” and “negatively biased.” Everything changed six months later. Hinton and two grad students used a network like the one LeCun made for reading checks to rout the field in the leading contest for image recognition. Known as the ImageNet Large Scale Visual Recognition Challenge, it asks software to identify 1,000 types of objects as diverse as mosquito nets and mosques. The Toronto entry correctly identified the object in an image within five guesses about 85 percent of the time, more than 10 percentage points better than the second-best system. The deep-learning software’s initial layers of neurons optimized themselves for finding simple things like edges and corners, with the layers after that looking for successively more complex features like basic shapes and, eventually, dogs or people. LeCun recalls seeing the community that had mostly ignored neural networks pack into the room where the winners presented a paper on their results. “You could see right there a lot of senior people in the community just flipped,” he says. “They said, ‘Okay, now we buy it. That’s it, now—you won.’” Academics working on computer vision quickly abandoned their old methods, and deep learning suddenly became one of the main strands in artificial intelligence. Google bought a company founded by Hinton and the two others behind the 2012 result, and Hinton started working there part time on a research team known as Google Brain. Microsoft and other companies created new projects to investigate deep learning. In December 2013, Facebook CEO Mark Zuckerberg stunned academics by showing up at the largest neural-network research conference, hosting a party where he announced that LeCun was starting FAIR (though he still works at NYU one day a week). LeCun still harbors mixed feelings about the 2012 research that brought the world around to his point of view. “To some extent this should have come out of my lab,” he says. Hinton shares that assessment. “It was a bit unfortunate for Yann that he wasn’t the one who actually made the breakthrough system,” he says. LeCun’s group had done more work than anyone else to prove out the techniques used to win the ImageNet challenge. The victory could have been his had student graduation schedules and other commitments not prevented his own group from taking on ImageNet, he says. LeCun’s hunt for deep learning’s next breakthrough is now a chance to even the score. Language learning Facebook’s New York office is a three-minute stroll up Broadway from LeCun’s office at NYU, on two floors of a building constructed as a department store in the early 20th century. Workers are packed more densely into the open plan than they are at Facebook’s headquarters in Menlo Park, California, but they can still be seen gliding on articulated skateboards past notices for weekly beer pong. Almost half of LeCun’s team of leading AI researchers works here, with the rest at Facebook’s California campus or an office in Paris. Many of them are trying to make neural networks better at understanding language. “I’ve hired all the people working on this that I could,” says LeCun. A neural network can “learn” words by spooling through text and calculating how each word it encounters could have been predicted from the words before or after it. By doing this, the software learns to represent every word as a vector that indicates its relationship to other words—a process that uncannily captures concepts in language. The difference between the vectors for “king” and “queen” is the same as for “husband” and “wife,” for example. The vectors for “paper” and “cardboard” are close together, and those for “large” and “big” are even closer. The same approach works for whole sentences (Hinton says it generates “thought vectors”), and Google is looking at using it to bolster its automatic translation service. A recent paper from researchers at a Chinese university and Microsoft’s Beijing lab used a version of the vector technique to make software that beats some humans on IQ-test questions requiring an understanding of synonyms, antonyms, and analogies. LeCun’s group is working on going further. “Language in itself is not that complicated,” he says. “What’s complicated is having a deep understanding of language and the world that gives you common sense. That’s what we’re really interested in building into machines.” LeCun means common sense as Aristotle used the term: the ability to understand basic physical reality. He wants a computer to grasp that the sentence “Yann picked up the bottle and walked out of the room” means the bottle left with him. Facebook’s researchers have invented a deep-learning system called a memory network that displays what may be the early stirrings of common sense. A memory network is a neural network with a memory bank bolted on to store facts it has learned so they don’t get washed away every time it takes in fresh data. The Facebook AI lab has created versions that can answer simple common-sense questions about text they have never seen before. For example, when researchers gave a memory network a very simplified summary of the plot of Lord of the Rings , it could answer questions such as “Where is the ring?” and “Where was Frodo before Mount Doom?” It could interpret the simple world described in the text despite having never previously encountered many of the names or objects, such as “Frodo” or “ring.” The software learned its rudimentary common sense by being shown how to answer questions about a simple text in which characters do things in a series of rooms, such as “Fred moved to the bedroom and Joe went to the kitchen.” But LeCun wants to expose the software to texts that are far better at capturing the complexity of life and the things a virtual assistant might need to do. A virtual concierge called Moneypenny that Facebook is expected to release could be one source of that data. The assistant is said to be powered by a team of human operators who will help people do things like make restaurant reservations. LeCun’s team could have a memory network watch over Moneypenny’s shoulder before eventually letting it learn by interacting with humans for itself. Several companies have opened deep-learning labs. “I’ve hired all the people working on this that I could,” says LeCun. Building something that can hold even a basic, narrowly focused conversation still requires significant work. For example, neural networks have shown only very simple reasoning, and researchers haven’t figured out how they might be taught to make plans, says LeCun. But results from the work that has been done with the technology so far leave him confident about where things are going. “The revolution is on the way,” he says. Some people are less sure. Deep-learning software so far has displayed only the simplest capabilities required for what we would recognize as conversation, says Oren Etzioni , CEO of the Allen Institute for Artificial Intelligence in Seattle. The logic and planning capabilities still needed, he says, are very different from the things neural networks have been doing best: digesting sequences of pixels or acoustic waveforms to decide which image category or word they represent. “The problems of understanding natural language are not reducible in the same way,” he says. Gary Marcus , a professor of psychology and neural science at NYU who has studied how humans learn language and recently started an artificial-intelligence company called Geometric Intelligence, thinks LeCun underestimates how hard it would be for existing software to pick up language and common sense. Training the software with large volumes of carefully annotated data is fine for getting it to sort images. But Marcus doubts it can acquire the trickier skills needed for language, where the meanings of words and complex sentences can flip depending on context. “People will look back on deep learning and say this is a really powerful technique—it’s the first time that AI became practical,” he says. “They’ll also say those things required a lot of data, and there were domains where people just never had enough.” Marcus thinks language may be one of those domains. For software to master conversation, it would need to learn more like a toddler who picks it up without explicit instruction, he suggests. Deep belief At Facebook’s headquarters in California, the West Coast members of LeCun’s team sit close to Mark Zuckerberg and Mike Schroepfer, the company’s CTO. Facebook’s leaders know that LeCun’s group is still some way from building something you can talk to, but Schroepfer is already thinking about how to use it. The future Facebook he describes retrieves and coördinates information, like a butler you communicate with by typing or talking as you might with a human one. “You can engage with a system that can really understand concepts and language at a much higher level,” says Schroepfer. He imagines being able to ask that you see a friend’s baby snapshots but not his jokes, for example. “I think in the near term a version of that is very realizable,” he says. As LeCun’s systems achieve better reasoning and planning abilities, he expects the conversation to get less one-sided. Facebook might offer up information that it thinks you’d like and ask what you thought of it. “Eventually it is like this super-intelligent helper that’s plugged in to all the information streams in the world,” says Schroepfer. It’s not clear how much we’d benefit from smarter virtual assistants, but we may not have to wait long to find out. The algorithms needed to power such interactions would also improve the systems Facebook uses to filter the posts and ads we see. And they could be vital to Facebook’s ambitions to become much more than just a place to socialize. As Facebook begins to host articles and video on behalf of media and entertainment companies, for example, it will need better ways for people to manage information. Virtual assistants and other spinouts from LeCun’s work could also help Facebook’s more ambitious departures from its original business, such as the Oculus group working to make virtual reality into a mass–market technology. None of this will happen if the recent impressive results meet the fate of previous big ideas in artificial intelligence. Blooms of excitement around neural networks have withered twice already. But while complaining that other companies or researchers are over-hyping their work is one of LeCun’s favorite pastimes , he says there’s enough circumstantial evidence to stand firm behind his own predictions that deep learning will deliver impressive payoffs. The technology is still providing more accuracy and power in every area of AI where it has been applied, he says. New ideas are needed about how to apply it to language processing, but the still-small field is expanding fast as companies and universities dedicate more people to it. “That will accelerate progress,” says LeCun. It’s still not clear that deep learning can deliver anything like the information butler Facebook envisions. And even if it can, it’s hard to say how much the world really would benefit from it. But we may not have to wait long to find out. LeCun guesses that virtual helpers with a mastery of language unprecedented for software will be available in just two to five years. He expects that anyone who doubts deep learning’s ability to master language will be proved wrong even sooner. “There is the same phenomenon that we were observing just before 2012,” he says. “Things are starting to work, but the people doing more classical techniques are not convinced. Within a year or two it will be the end.” hide by Tom Simonite Share linkedinlink opens in a new window twitterlink opens in a new window facebooklink opens in a new window emaillink opens in a new window This story was part of our September/October 2015 issue. Popular This new data poisoning tool lets artists fight back against generative AI Melissa Heikkilä Everything you need to know about artificial wombs Cassandra Willyard Deepfakes of Chinese influencers are livestreaming 24/7 Zeyi Yang How to fix the internet Katie Notopoulos Deep Dive Artificial intelligence This new data poisoning tool lets artists fight back against generative AI The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models. By Melissa Heikkilä archive page Deepfakes of Chinese influencers are livestreaming 24/7 With just a few minutes of sample video and $1,000, brands never have to stop selling their products. By Zeyi Yang archive page Driving companywide efficiencies with AI Advanced AI and ML capabilities revolutionize how administrative and operations tasks are done. 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1,926	2,023	"Trump Squeezed America's Geek Squad. Biden Built It Back Stronger \| WIRED"	"https://www.wired.com/story/plaintext-trump-squeezed-americas-geek-squad-biden-built-it-back-stronger"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Steven Levy Business Trump Squeezed America's Geek Squad. Biden Built It Back Stronger Mina Hsiang, administrator of the United States Digital Service. Courtesy of Imagine Photography DC Save this story Save Save this story Save Mina Hsiang returned to the United States Digital Service , the US government's rapid digital fix-it squad, on January 26, 2021, when the streets of Washington, DC, had hardly been cleared after Joe Biden’s inauguration. She was one of the group’s founding members but had spent the past few years working for a health care startup. Upon her return, Hsiang worked on Covid response, and in September 2021, she became the third administrator of the USDS. Her timing was impeccable. The organization had sprung from the infamous HeathCare.gov debacle in 2013, when the website for selecting insurance plans under the new Obamacare law crashed badly. Hsiang was a key member of the scrappy rescue team that turned things around, using principles of web design that were common in Silicon Valley operations but underutilized in government. Their methods flew in the face of typical arrangements in federal agencies, which would contract out digital operations to legacy firms with Beltway connections. Those six- or seven-figure contracts seldom demanded benchmark performances and often took years to complete, or were never finished at all. The tiny team of idealistic rescuers not only helped design a cleaner avenue to health insurance, but charmed the lifers at Health and Human Services (HHS) into enlisting them to fix up digital government more broadly. The idea behind the new USDS was to bottle the same guerilla spirit that had saved HealthCare.gov. Ideally, these volunteers from the commercial tech firms would win the hearts and minds of people inside agencies like the Veterans Affairs (VA) or HHS, infiltrating their calcified cultures with the can-do spirit and constant iteration of a startup and creating digital government services as slick as the latest app from Silicon Valley. I spoke to Hsiang this week about how the USDS is faring after two years under her leadership. During the Trump years, the agency had to scramble just to stay alive, no easy task when a target was tacked onto anything even tangentially related to Obama. The team survived through a combination of lying low and doing productive work. They managed to thread that needle, in part, because Jared Kushner was at one point infatuated with the concept. Nonetheless, USDS wasn’t thriving when Hsiang returned. “The last administration had done a lot to undermine staffing,” she says. Hsiang took over just as things were looking up. Biden’s 2021 American Rescue Plan directed an astonishing $200 million to the USDS, ballooning its previously modest budget. That enabled USDS coders and designers to work with more agencies and start new programs. “There was just a ton of demand across government. So it was, ‘OK, how do we rebuild, scale, and up level,’” says Hsiang. It also helped that late in 2021, Biden issued an executive order making human-centered design a key part of the federal government’s digital interface with citizens. One radical idea: “In all sectors, services should reduce burdens, not increase them.” The head count of USDS is now around 215, up from 80 when Hsiang ended her first stint with the group. “About a third of those are returners,” Hsiang says. Despite what she calls the “anti-sell”—a warning about the restrictions and financial implications of working for the government—“People still want to show up.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Another part of her task was steadying the ship. Despite a number of victories in agencies ranging from the VA to the Department of Defense , USDS has enemies. Not surprisingly, some of those fat-cat contractors who enjoyed no-blame deals to create bloated databases that didn’t work pushed to constrain or kill this threat to their business models and self-respect. And apparently some critics just don’t like the idea of people in hoodies churning out code in the basements of federal agencies. The USDS has always dealt with pushback in Congress, and this summer some legislators launched an unsuccessful (for now) effort to strip $80 million from the USDS budget, claiming that the service wasn’t accountable. “What the hell are they working on?” one anonymous government critic said to FedScoop. It’s actually pretty easy to see what the USDS is working on if you know where to look. You can find their work, for instance, on the Social Security Administration homepage , which has been revamped and streamlined with USDS input. “In November of last year it had 70,000 pages for you to navigate to find information,” Hsiang says. “We got it down to 280, which is much more digestible.” Or consider the website that allowed Americans to order home delivery of free Covid tests. Instead of asking people dozens of questions before they could sign up, the drop-dead simple form just asked where to send the darn things. Yes, there was a speed bump when the site couldn’t parse some addresses for citizens who lived in multifamily residences, but that was quickly resolved. Two-thirds of American households ultimately participated, with over 755 million tests distributed. “It was a phenomenal example of the partnership between USDS and agencies and the White House and the US Postal Service—of how we can all work together,” says Hsiang. “We can restore trust by having a thing that operates as you would expect it to, that looks more like the products we all choose to use every day, rather than the ones we have to use.” There’s a long way to go, of course. Matthew Desmond, in his book Poverty by America , describes how millions of Americans don’t take advantage of vital programs because they are difficult to access. “I think a lot about the opportunity for technology to reduce that administrative burden,” says Hsiang. One problem, she notes, is that getting help often requires a citizen to access programs from multiple agencies that are poorly coordinated. “One of our superpowers is our ability to work between multiple agencies.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg One missed opportunity is the failure of the Biden Administration to fill the post of chief technology officer of the United States. “It would definitely be better to have an incredible partner in that office,” Hsiang concedes. On the other hand, Biden’s current chief of staff, Jeff Zients, is deeply familiar with USDS, since he was once in charge of the HealthCare.gov rescue. “He brings us in and ensures that programs are running the right way,” Hsiang says. I ask Hsiang how USDS regards generative AI because, well, my license as a tech pundit would be revoked if I failed to do that. “We’re looking at it very carefully,” she says—a line currently mandatory for those in her line of work. She cites concerns that AI bots might infect services with bias. But like it or not, the AI boom has to be dealt with. Hsiang cites an HHS website called Grants.gov that takes submissions for thousands of funding applications. A flood of AI-generated pitches is expected. “We need to respond to that,” she says. The USDS is also experimenting with ways to use generative AI inside government services. “We’re hiring for folks who really understand how to use and implement AI systems,” she says. One thing hasn’t changed at USDS: its desire to spread a positive contagion of citizen-centric tech efforts among those bureaucracies. “One of our hypotheses early on is to see if we can do this culture change, with different ways of operating and thinking, and make it sustainable,” says Hsiang. “We’re currently working with about a dozen agencies who are trying to think through how they can build that capability internally.” One indicator of this shift: The patient Hsiang first joined the government to save is thriving. Transcending its disastrous beginning, HealthCare.gov no longer requires outside support from the group’s geeky fixers. In January 2017, I wrote about the United States Digital Service’s accomplishments, as well as its uncertain prospects under a president who might not be inclined to continue the Obama-created agency of tech hackers dedicated to Silicon Valley-izing government IT. As the inauguration approaches, the mood swings at the USDS are Calder-esque. Dickerson describes it as “a high school graduation and a massive layoff mixed with a funeral that’s gone on for two months.” On the Facebook feeds of politically appointed tech surgers you see photos of final handshakes with the president; they’re wearing uncharacteristically formal garb and are often with their families; they have been ushered into the Oval Office for mutual thanks. Obama himself bid farewell to the team at a ceremony on the steps of the Executive Office Building last Thursday. He spent the better part of an hour thanking the team and telling them what a difference they made. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But they know it already, and the experience has made many of them reluctant to return to their previous lives inside profit-making corporations. Those jobs don’t seem so meaningful anymore. Some are sticking around the DC area, even though they hate it as a place to live. There’s talk about a loose network of tech surge alumni engaging in a new kind of insurgency—outside the government but with the same end of serving the people. “Every hint I ever had was that the infrastructure of civilization was someone else’s problem,” says Matthew Weaver. “What a lie that was. It was my problem. I’m lucky to have the skills to address this. Now I want everyone who has an inkling of this to understand … to say, this is my problem.” Erica asks, “Would it be possible to create a transcription application (implanted or external) that could record your musings, whether vocalized or internal, while still protecting privacy? The idea of this application would be that I could turn on the app for that moment, but not continuously.” Hi, Erica, thanks for asking. As you imply, the brain is too leaky to preserve our best thoughts, not to mention memories that could be of use to us, or at least entertain us and indulge our nostalgia. Technologists have been trying for years to come up with a system to record everything, and in recent years some have come on the market, including an app called Rewind , to record huge volumes of what you see, hear, and read. Eventually, if all goes well, you accumulate a lifetime of memories and business interactions, stored “offline,” meaning not in your head. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Your requirements, Erica, seem less ambitious: to capture only the thoughts you consciously decide are worthy of retention. If that’s what you want, the “implanted” device you mention is overkill. You might as well use a cheap digital recorder and flip it on. But what about privacy? I asked Dan Siroker, the founder and CEO of Rewind. (Its capture mechanism isn’t a brain implant, but the iPhone.) He says that his company’s security strategy rests on keeping the data on your private device, not in some cloud system. Because Rewind compresses raw data up to 3,750 times, a cheap hard drive could store a lifetime’s worth of conversations and dictations. Of course, if it’s available to you, others could gain access to it, whether through hacking, theft, or subpoena. So the answer to your question is a qualified yes—an app that records your transcription can protect your privacy, but only to the extent that any data security plan can be relied on. Siroker also thinks that you should reconsider your plan to selectively capture your thoughts. “I highly recommend keeping it all and sifting through it as needed,” he says. I suspect you may not take his advice. You can submit questions to [email protected]. Write ASK LEVY in the subject line. Back to school becomes stay at home as the hottest summer ever won’t go away. My deep dive into OpenAI , the company that believes in superintelligence so much that it has an escape clause in its contracts that demands a reset if AGI arrives and changes everything. In her Big Interview with WIRED’s Kate Knibbs, Naomi Klein says why she’s not just crying Wolf. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The “Mother of Design ” had a secret weapon: empathy. A massive hack from China exposes Microsoft as the ( Public) Keystone Cops. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Learn more. Updated 9-8-2023, 7:45 pm EDT: Mina Hsiang is the United States Digital Service's administrator, not its director. 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1,927	2,016	"Addicted to Your iPhone? You’re Not Alone - The Atlantic"	"https://www.theatlantic.com/magazine/archive/2016/11/the-binge-breaker/501122"	"Site Navigation The Atlantic Popular Latest Newsletters Sections Politics Ideas Fiction Technology Science Photo Business Culture Planet Global Books Podcasts Health Education Projects Features Family Events Washington Week Progress Newsletters Explore The Atlantic Archive Play The Atlantic crossword The Print Edition Latest Issue Past Issues Give a Gift Search The Atlantic Quick Links Dear Therapist Crossword Puzzle Magazine Archive Your Subscription Popular Latest Newsletters Sign In Subscribe Explore The Tech Issue: The view from Silicon Valley, how social media is changing war, and breaking your internet addiction. Plus, a rare presidential endorsement, Jane Jacobs on the fragility of democracy, and much more War Goes Viral Emerson T. Brooking and P. W. Singer Against Donald Trump The Editors The Binge Breaker Bianca Bosker The View From the Valley The Editors A Pocket Guide to the Robot Revolution Ian Bogost How America Outlawed Adolescence Amanda Ripley A gift that gets them talking. Give a year of stories to spark conversation, Plus a free tote. The Binge Breaker Tristan Harris believes Silicon Valley is addicting us to our phones. He’s determined to make it stop. O n a recent evening in San Francisco, Tristan Harris, a former product philosopher at Google, took a name tag from a man in pajamas called “Honey Bear” and wrote down his pseudonym for the night: “Presence.” Harris had just arrived at Unplug SF, a “digital detox experiment” held in honor of the National Day of Unplugging, and the organizers had banned real names. Also outlawed: clocks, “w-talk” (work talk), and “WMDs” (the planners’ loaded shorthand for wireless mobile devices ). Harris, a slight 32-year-old with copper hair and a tidy beard, surrendered his iPhone, a device he considers so addictive that he’s called it “a slot machine in my pocket.” He keeps the background set to an image of Scrabble tiles spelling out the words face down , a reminder of the device’s optimal position. I followed him into a spacious venue packed with nearly 400 people painting faces, filling in coloring books, and wrapping yarn around chopsticks. Despite the cheerful summer-camp atmosphere, the event was a reminder of the binary choice facing smartphone owners, who, according to one study, consult their device 150 times a day: Leave the WMD on and deal with relentless prompts compelling them to check its screen, or else completely disconnect. “It doesn’t have to be the all-or-nothing choice,” Harris told me after taking in the arts-and-crafts scene. “That’s a design failure.” Harris is the closest thing Silicon Valley has to a conscience. As the co‑founder of Time Well Spent, an advocacy group, he is trying to bring moral integrity to software design: essentially, to persuade the tech world to help us disengage more easily from its devices. While some blame our collective tech addiction on personal failings, like weak willpower, Harris points a finger at the software itself. That itch to glance at our phone is a natural reaction to apps and websites engineered to get us scrolling as frequently as possible. The attention economy, which showers profits on companies that seize our focus, has kicked off what Harris calls a “race to the bottom of the brain stem.” “You could say that it’s my responsibility” to exert self-control when it comes to digital usage, he explains, “but that’s not acknowledging that there’s a thousand people on the other side of the screen whose job is to break down whatever responsibility I can maintain.” In short, we’ve lost control of our relationship with technology because technology has become better at controlling us. Under the auspices of Time Well Spent, Harris is leading a movement to change the fundamentals of software design. He is rallying product designers to adopt a “Hippocratic oath” for software that, he explains, would check the practice of “exposing people’s psychological vulnerabilities” and restore “agency” to users. “There needs to be new ratings, new criteria, new design standards, new certification standards,” he says. “There is a way to design based not on addiction.” Joe Edelman—who did much of the research informing Time Well Spent’s vision and is the co-director of a think tank advocating for more-respectful software design—likens Harris to a tech-focused Ralph Nader. Other people, including Adam Alter, a marketing professor at NYU, have championed theses similar to Harris’s; but according to Josh Elman, a Silicon Valley veteran with the venture-capital firm Greylock Partners, Harris is “the first putting it together in this way”—articulating the problem, its societal cost, and ideas for tackling it. Elman compares the tech industry to Big Tobacco before the link between cigarettes and cancer was established: keen to give customers more of what they want, yet simultaneously inflicting collateral damage on their lives. Harris, Elman says, is offering Silicon Valley a chance to reevaluate before more-immersive technology, like virtual reality, pushes us beyond a point of no return. A ll this talk of hacking human psychology could sound paranoid, if Harris had not witnessed the manipulation firsthand. Raised in the Bay Area by a single mother employed as an advocate for injured workers, Harris spent his childhood creating simple software for Macintosh computers and writing fan mail to Steve Wozniak, a co-founder of Apple. He studied computer science at Stanford while interning at Apple, then embarked on a master’s degree at Stanford, where he joined the Persuasive Technology Lab. Run by the experimental psychologist B. J. Fogg, the lab has earned a cultlike following among entrepreneurs hoping to master Fogg’s principles of “behavior design”—a euphemism for what sometimes amounts to building software that nudges us toward the habits a company seeks to instill. (One of Instagram’s co-founders is an alumnus.) In Fogg’s course, Harris studied the psychology of behavior change, such as how clicker training for dogs, among other methods of conditioning, can inspire products for people. For example, rewarding someone with an instantaneous “like” after they post a photo can reinforce the action, and potentially shift it from an occasional to a daily activity. Harris learned that the most-successful sites and apps hook us by tapping into deep-seated human needs. When LinkedIn launched, for instance, it created a hub-and-spoke icon to visually represent the size of each user’s network. That triggered people’s innate craving for social approval and, in turn, got them scrambling to connect. “Even though at the time there was nothing useful you could do with LinkedIn, that simple icon had a powerful effect in tapping into people’s desire not to look like losers,” Fogg told me. Harris began to see that technology is not, as so many engineers claim, a neutral tool; rather, it’s capable of coaxing us to act in certain ways. And he was troubled that out of 10 sessions in Fogg’s course, only one addressed the ethics of these persuasive tactics. (Fogg says that topic is “woven throughout” the curriculum.) Explore the November 2016 Issue Check out more from this issue and find your next story to read. Harris dropped out of the master’s program to launch a start-up that installed explanatory pop-ups across thousands of sites, including The New York Times ’. It was his first direct exposure to the war being waged for our time, and Harris felt torn between his company’s social mission, which was to spark curiosity by making facts easily accessible, and pressure from publishers to corral users into spending more and more minutes on their sites. Though Harris insists he steered clear of persuasive tactics, he grew more familiar with how they were applied. He came to conceive of them as “hijacking techniques”—the digital version of pumping sugar, salt, and fat into junk food in order to induce bingeing. McDonald’s hooks us by appealing to our bodies’ craving for certain flavors; Facebook, Instagram, and Twitter hook us by delivering what psychologists call “variable rewards.” Messages, photos, and “likes” appear on no set schedule, so we check for them compulsively, never sure when we’ll receive that dopamine-activating prize. (Delivering rewards at random has been proved to quickly and strongly reinforce behavior.) Checking that Facebook friend request will take only a few seconds, we reason, though research shows that when interrupted, people take an average of 25 minutes to return to their original task. Sites foster a sort of distracted lingering partly by lumping multiple services together. To answer the friend request, we’ll pass by the News Feed, where pictures and auto-play videos seduce us into scrolling through an infinite stream of posts—what Harris calls a “bottomless bowl,” referring to a study that found people eat 73 percent more soup out of self-refilling bowls than out of regular ones, without realizing they’ve consumed extra. The “friend request” tab will nudge us to add even more contacts by suggesting “people you may know,” and in a split second, our unconscious impulses cause the cycle to continue: Once we send the friend request, an alert appears on the recipient’s phone in bright red—a “trigger” color, Harris says, more likely than some other hues to make people click—and because seeing our name taps into a hardwired sense of social obligation, she will drop everything to answer. In the end, he says, companies “stand back watching as a billion people run around like chickens with their heads cut off, responding to each other and feeling indebted to each other.” A Facebook spokesperson told me the social network focuses on maximizing the quality of the experience—not the time its users spend on the site—and surveys its users daily to gauge success. In response to this feedback, Facebook recently tweaked its News Feed algorithm to punish clickbait—stories with sensationalist headlines designed to attract readers. (LinkedIn and Instagram declined requests for comment. Twitter did not reply to multiple queries.) Even so, a niche group of consultants has emerged to teach companies how to make their services irresistible. One such guru is Nir Eyal , the author of Hooked: How to Build Habit-Forming Products , who has lectured or consulted for firms such as LinkedIn and Instagram. A blog post he wrote touting the value of variable rewards is titled “Want to Hook Your Users? Drive Them Crazy.” While asserting that companies are morally obligated to help those genuinely addicted to their services, Eyal contends that social media merely satisfies our appetite for entertainment in the same way TV or novels do, and that the latest technology tends to get vilified simply because it’s new, but eventually people find balance. “Saying ‘Don’t use these techniques’ is essentially saying ‘Don’t make your products fun to use.’ That’s silly,” Eyal told me. “With every new technology, the older generation says ‘Kids these days are using too much of this and too much of that and it’s melting their brains.’ And it turns out that what we’ve always done is to adapt.” Google acquired Harris’s company in 2011, and he ended up working on Gmail’s Inbox app. (He’s quick to note that while he was there, it was never an explicit goal to increase time spent on Gmail.) A year into his tenure, Harris grew concerned about the failure to consider how seemingly minor design choices, such as having phones buzz with each new email, would cascade into billions of interruptions. His team dedicated months to fine-tuning the aesthetics of the Gmail app with the aim of building a more “delightful” email experience. But to him that missed the bigger picture: Instead of trying to improve email, why not ask how email could improve our lives—or, for that matter, whether each design decision was making our lives worse? Six months after attending Burning Man in the Nevada desert, a trip Harris says helped him with “waking up and questioning my own beliefs,” he quietly released “A Call to Minimize Distraction & Respect Users’ Attention,” a 144-page Google Slides presentation. In it, he declared, “Never before in history have the decisions of a handful of designers (mostly men, white, living in SF, aged 25–35) working at 3 companies”—Google, Apple, and Facebook—“had so much impact on how millions of people around the world spend their attention … We should feel an enormous responsibility to get this right.” Although Harris sent the presentation to just 10 of his closest colleagues, it quickly spread to more than 5,000 Google employees, including then-CEO Larry Page, who discussed it with Harris in a meeting a year later. “It sparked something,” recalls Mamie Rheingold, a former Google staffer who organized an internal Q&A session with Harris at the company’s headquarters. “He did successfully create a dialogue and open conversation about this in the company.” Harris parlayed his presentation into a position as product philosopher, which involved researching ways Google could adopt ethical design. But he says he came up against “inertia.” Product road maps had to be followed, and fixing tools that were obviously broken took precedence over systematically rethinking services. Chris Messina, then a designer at Google, says little changed following the release of Harris’s slides: “It was one of those things where there’s a lot of head nods, and then people go back to work.” Harris told me some colleagues misinterpreted his message, thinking that he was proposing banning people from social media, or that the solution was simply sending fewer notifications. (Google declined to comment.) Harris left the company last December to push for change more widely, buoyed by a growing network of supporters that includes the MIT professor Sherry Turkle; Meetup’s CEO, Scott Heiferman; and Justin Rosenstein, a co-inventor of the “like” button; along with fed-up users and concerned employees across the industry. “Pretty much every big company that’s manipulating users has been very interested in our work,” says Joe Edelman, who has spent the past five years trading ideas and leading workshops with Harris. Through Time Well Spent, his advocacy group, Harris hopes to mobilize support for what he likens to an organic-food movement, but for software: an alternative built around core values, chief of which is helping us spend our time well, instead of demanding more of it. Thus far, Time Well Spent is more a label for his crusade—and a vision he hopes others will embrace—than a full-blown organization. (Harris, its sole employee, self-funds it.) Yet he’s amassed a network of volunteers keen to get involved, thanks in part to his frequent cameos on the thought-leader speaker circuit, including talks at Harvard’s Berkman Klein Center for Internet & Society; the O’Reilly Design Conference; an internal meeting of Facebook designers; and a TEDx event, whose video has been viewed more than 1 million times online. Tim O’Reilly, the founder of O’Reilly Media and an early web pioneer, told me Harris’s ideas are “definitely something that people who are influential are listening to and thinking about.” Even Fogg, who stopped wearing his Apple Watch because its incessant notifications annoyed him, is a fan of Harris’s work: “It’s a brave thing to do and a hard thing to do.” A t Unplug SF, a burly man calling himself “Haus” enveloped Harris in a bear hug. “This is the antidote!,” Haus cheered. “This is the antivenom!” All evening, I watched people pull Harris aside to say hello, or ask to schedule a meeting. Someone cornered Harris to tell him about his internet “sabbatical,” but Harris cut him off. “For me this is w‑talk,” he protested. Harris admits that researching the ways our time gets hijacked has made him slightly obsessive about evaluating what counts as “time well spent” in his own life. The hypnosis class Harris went to before meeting me—because he suspects the passive state we enter while scrolling through feeds is similar to being hypnotized—was not time well spent. The slow-moving course, he told me, was “low bit rate”—a technical term for data-transfer speeds. Attending the digital detox? Time very well spent. He was delighted to get swept up in a mass game of rock-paper-scissors, where a series of one-on-one elimination contests culminated in an onstage showdown between “Joe” and “Moonlight.” Harris has a tendency to immerse himself in a single activity at a time. In conversation, he rarely breaks eye contact and will occasionally rest a hand on his interlocutor’s arm, as if to keep both parties present in the moment. He got so wrapped up in our chat one afternoon that he attempted to get into an idling Uber that was not an Uber at all, but a car that had paused at a stop sign. An accordion player and tango dancer in his spare time who pairs plaid shirts with a bracelet that has presence stamped into a silver charm, Harris gives off a preppy-hippie vibe that allows him to move comfortably between Palo Alto boardrooms and device-free retreats. In that sense, he had a great deal in common with the other Unplug SF attendees, many of whom belong to a new class of tech elites “waking up” to their industry’s unwelcome side effects. For many entrepreneurs, this epiphany has come with age, children, and the peace of mind of having several million in the bank, says Soren Gordhamer, the creator of Wisdom 2.0, a conference series about maintaining “presence and purpose” in the digital age. “They feel guilty,” Gordhamer says. “They are realizing they built this thing that’s so addictive.” I asked Harris whether he felt guilty about having joined Google, which has inserted its technology into our pockets, glasses, watches, and cars. He didn’t. He acknowledged that some divisions, such as YouTube, benefit from coaxing us to stare at our screens. But he justified his decision to work there with the logic that since Google controls three interfaces through which millions engage with technology—Gmail, Android, and Chrome—the company was the “first line of defense.” Getting Google to rethink those products, as he’d attempted to do, had the potential to transform our online experience. At a restaurant around the corner from Unplug SF, Harris demonstrated an alternative way of interacting with WMDs, based on his own self-defense tactics. Certain tips were intuitive: He’s “almost militaristic about turning off notifications” on his iPhone, and he set a custom vibration pattern for text messages, so he can feel the difference between an automated alert and a human’s words. Other tips drew on Harris’s study of psychology. Since merely glimpsing an app’s icon will “trigger this whole set of sensations and thoughts,” he pruned the first screen of his phone to include only apps, such as Uber and Google Maps, that perform a single function and thus run a low risk of “bottomless bowl–ing.” He tried to make his phone look minimalist: Taking a cue from a Google experiment that cut employees’ M&M snacking by moving the candy from clear to opaque containers, he buried colorful icons—along with time-sucking apps like Gmail and WhatsApp—inside folders on the second page of his iPhone. As a result, that screen was practically grayscale. Harris launches apps by using what he calls the phone’s “consciousness filter”—typing Instagram , say, into its search bar—which reduces impulsive tapping. For similar reasons, Harris keeps a Post-it on his laptop with this instruction: “Do not open without intention.” His approach seems to have worked. I’m usually quick to be annoyed by friends reaching for their phones, but next to Harris, I felt like an addict. Wary of being judged, I made a point not to check my iPhone unless he checked his first, but he went so long without peeking that I started getting antsy. Harris assured me that I was far from an exception. “Our generation relies on our phones for our moment-to-moment choices about who we’re hanging out with, what we should be thinking about, who we owe a response to, and what’s important in our lives,” he said. “And if that’s the thing that you’ll outsource your thoughts to, forget the brain implant. That is the brain implant. You refer to it all the time.” C urious to hear more about Harris’s plan for tackling manipulative software, I tagged along one morning to his meeting with two entrepreneurs eager to incorporate Time Well Spent values into their start-up. Harris, flushed from a yoga class, met me at a bakery not far from the “intentional community house” where he lives with a dozen or so housemates. We were joined by Micha Mikailian and Johnny Chan, the co-founders of an ad blocker, Intently, that replaces advertising with “intentions” reminding people to “Follow Your Bliss” or “Be Present.” Previously, they’d run a marketing and advertising agency. “One day I was in a meditation practice. I just got the vision for Intently,” said Mikailian, who sported a chunky turquoise bracelet and a man bun. “It fully aligned with my purpose,” said Chan. They were interested in learning what it would take to integrate ethical design. Coordinating loosely with Joe Edelman, Harris is developing a code of conduct—the Hippocratic oath for software designers—and a playbook of best practices that can guide start-ups and corporations toward products that “treat people with respect.” Having companies rethink the metrics by which they measure success would be a start. “You have to imagine: What are the concrete benefits landed in space and in time in a person’s life?,” Harris said, coaching Mikailian and Chan. At his speaking engagements, Harris has presented prototype products that embody other principles of ethical design. He argues that technology should help us set boundaries. This could be achieved by, for example, an inbox that asks how much time we want to dedicate to email, then gently reminds us when we’ve exceeded our quota. Technology should give us the ability to see where our time goes, so we can make informed decisions—imagine your phone alerting you when you’ve unlocked it for the 14th time in an hour. And technology should help us meet our goals, give us control over our relationships, and enable us to disengage without anxiety. Harris has demoed a hypothetical “focus mode” for Gmail that would pause incoming messages until someone has finished concentrating on a task, while allowing interruptions in case of an emergency. (Slack has implemented a similar feature.) Harris hopes to create a Time Well Spent certification—akin to the leed seal or an organic label—that would designate software made with those values in mind. He already has a shortlist of apps that he endorses as early exemplars of the ethos, such as Pocket, Calendly, and f.lux, which, respectively, saves articles for future reading, lets people book empty slots on an individual’s calendar to streamline the process of scheduling meetings, and aims to improve sleep quality by adding a pinkish cast to the circadian-rhythm-disrupting blue light of screens. Intently could potentially join this coalition, he volunteered. As a first step toward identifying other services that could qualify, Harris has experimented with creating software that would capture how many hours someone devotes weekly to each app on her phone, then ask her which ones were worthwhile. The data could be compiled to create a leaderboard that shames apps that addict but fail to satisfy. Edelman has released a related tool for websites, called Hindsight. “We have to change what it means to win,” Harris says. T he biggest obstacle to incorporating ethical design and “agency” is not technical complexity. According to Harris, it’s a “will thing.” And on that front, even his supporters worry that the culture of Silicon Valley may be inherently at odds with anything that undermines engagement or growth. “This is not the place where people tend to want to slow down and be deliberate about their actions and how their actions impact others,” says Jason Fried, who has spent the past 12 years running Basecamp, a project-management tool. “They want to make things more sugary and more tasty, and pull you in, and justify billions of dollars of valuation and hundreds of millions of dollars [in] VC funds.” Rather than dismantling the entire attention economy, Harris hopes that companies will, at the very least, create a healthier alternative to the current diet of tech junk food. He recognizes that this shift would require reevaluating entrenched business models so success no longer hinges on claiming attention and time. As with organic vegetables, it’s possible that the first generation of Time Well Spent software might be available at a premium price, to make up for lost advertising dollars. “Would you pay $7 a month for a version of Facebook that was built entirely to empower you to live your life?,” Harris says. “I think a lot of people would pay for that.” Like splurging on grass-fed beef, paying for services that are available for free and disconnecting for days (even hours) at a time are luxuries that few but the reasonably well-off can afford. I asked Harris whether this risked stratifying tech consumption, such that the privileged escape the mental hijacking and everyone else remains subjected to it. “It creates a new inequality. It does,” Harris admitted. But he countered that if his movement gains steam, broader change could occur, much in the way Walmart now stocks organic produce. Currently, though, the trend is toward deeper manipulation in ever more sophisticated forms. Harris fears that Snapchat’s tactics for hooking users make Facebook’s look quaint. Facebook automatically tells a message’s sender when the recipient reads the note—a design choice that, per Fogg’s logic, activates our hardwired sense of social reciprocity and encourages the recipient to respond. Snapchat ups the ante: Unless the default settings are changed, users are informed the instant a friend begins typing a message to them—which effectively makes it a faux pas not to finish a message you start. Harris worries that the app’s Snapstreak feature, which displays how many days in a row two friends have snapped each other and rewards their loyalty with an emoji, seems to have been pulled straight from Fogg’s inventory of persuasive tactics. Research shared with Harris by Emily Weinstein, a Harvard doctoral candidate, shows that Snapstreak is driving some teenagers nuts—to the point that before going on vacation, they give friends their log-in information and beg them to snap in their stead. “To be honest, it made me sick to my stomach to hear these anecdotes,” Harris told me. Harris thinks his best shot at improving the status quo is to get users riled up about the ways they’re being manipulated, then create a groundswell of support for technology that respects people’s agency—something akin to the privacy outcry that prodded companies to roll out personal-information protections. While Harris’s experience at Google convinced him that users must demand change for it to happen, Edelman suggests that the incentive to adapt can originate within the industry, as engineers become reluctant to build products they view as unethical and companies face a brain drain. The more people recognize the repercussions of tech firms’ persuasive tactics, the more working there “becomes uncool,” he says, a view I heard echoed by others in his field. “You can really burn through engineers hard.” There is arguably an element of hypocrisy to the enlightened image that Silicon Valley projects, especially with its recent embrace of “mindfulness.” Companies like Google and Facebook, which have offered mindfulness training and meditation spaces for their employees, position themselves as corporate leaders in this movement. Yet this emphasis on mindfulness and consciousness, which has extended far beyond the tech world, puts the burden on users to train their focus, without acknowledging that the devices in their hands are engineered to chip away at their concentration. It’s like telling people to get healthy by exercising more, then offering the choice between a Big Mac and a Quarter Pounder when they sit down for a meal. And being aware of software’s seductive power does not mean being immune to its influence. One evening, just as we were about to part ways for the night, Harris stood talking by his car when his phone flashed with a new text message. He glanced down at the screen and interrupted himself mid-sentence. “Oh!” he announced, more to his phone than to me, and mumbled something about what a coincidence it was that the person texting him knew his friend. He looked back up sheepishly. “That’s a great example,” he said, waving his phone. “I had no control over the process.” "
1,928	2,017	"Meet YouTube's Hidden Laborers Toiling to Keep Ads Off Hateful Videos \| WIRED"	"https://www.wired.com/2017/04/zerochaos-google-ads-quality-raters"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Davey Alba Business The Hidden Laborers Training AI to Keep Ads Off Hateful YouTube Videos Getty Images Save this story Save Save this story Save Every day across the nation, people doing work for Google log on to their computers and start watching YouTube. They look for violence in videos. They seek out hateful language in video titles. They decide whether to classify clips as “offensive” or “sensitive.” They are Google’s so-called “ads quality raters,” temporary workers hired by outside agencies to render judgments machines still can't make all on their own. And right now, Google appears to need these humans' help—urgently. YouTube, the Google-owned video giant, sells ads that accompany millions of the site’s videos each day. Automated systems determine where those ads appear, and advertisers often don’t know which specific videos their ads will show up next to. Recently, that uncertainty has turned into a big problem for Google. The company has come under scrutiny after multiple reports revealed that it had allowed ads to run against YouTube videos promoting hate and terrorism. Advertisers such as Walmart, PepsiCo, and Verizon ditched the platform and much of the wider Google ad network. Google has scrambled to control the narrative , saying the media has overstated the problem of ads showing up adjacent to offensive videos. Flagged videos received “less than 1/1000th of a percent of the advertisers’ total impressions,” the company says. Google’s chief business officer, Philipp Schindler, says the issue affected a “very, very, very small” number of videos. But ad raters say the company is marshaling them as a force to keep the problem from getting worse. 'We know very well that human eyes—and human brains—need to put some deliberate thought into evaluating content.' Former Ad Rater Because Google derives 90 percent of its revenue from advertisers, it needs to keep more from fleeing by targeting offensive content—fast. But users upload nearly 600,000 hours of new video to YouTube daily; it would take a small city of humans working around the clock to watch it all. That’s why the tech giant has emphasized that it’s hard at work developing artificially intelligent content filters, software that can flag offensive videos at a greater clip than ever before. "The problem cannot be solved by humans and it shouldn't be solved by humans," Schindler recently told Bloomberg. The problem is, the company still needs humans to train that AI. So Google still depends on a phalanx of human workers to identify and flag offensive material to build the trove of data its AI will learn from. But eight current and former raters tell WIRED that, at a time when the company is increasingly reliant on ad raters' work, poor communication with Google and a lack of job stability are impairing their ability to do their jobs well. “I’m not saying this is the entire reason for the current crisis,” says a former Google ad rater, who was not authorized to speak with WIRED about the program. “But I do believe the instability in the program is a factor. We raters train AI, but we know very well that human eyes—and human brains—need to put some deliberate thought into evaluating content.” Tech companies have long employed content moderators; as people upload and share more and more content, this work has become increasingly important to these internet giants. The ad raters WIRED spoke with explained that their role goes beyond monitoring videos. They read comment sections to flag abusive banter between users. They check all kinds of websites served by Google’s ad network to ensure they meet the company’s standards of quality. They classify sites by category, such as retail or news, and click links in ads to see if they work. And, as their name suggests, they rate the quality of ads themselves. In March, however, in the wake of advertiser boycotts, Google asked raters to set that other work aside in favor of a "high-priority rating project” that would consume their workloads “for the foreseeable future,” according to an email the company sent them. This new project meant focusing almost exclusively on YouTube—checking the content of videos or entire channels against a list of things that advertisers find objectionable. “It’s been a huge change,” says one ad rater. 'I'm worried if I take too long on too many videos in a row I'll get fired.' Ad Rater Raters say their workload suggests that volume and speed are more of a priority than accuracy. In some cases, they’re asked to review hours-long videos in less than two minutes. On anonymous online forums, raters swap time-saving techniques—for instance, looking up rap video lyrics to scan quickly for profanity, or skipping through a clip in 10-second chunks instead of watching the entire thing. A timer keeps track of how long they spend on each video, and while it is only a suggested deadline, raters say it adds a layer of pressure. "I'm worried if I take too long on too many videos in a row I'll get fired,” one rater tells WIRED. Ad raters don’t just flag videos as inappropriate. They are asked to make granular assessments of their title and contents—classifying them, for instance, as containing “Inappropriate Language,” such as “profanity,” “hate speech,” or “other.” Or “Violence,” with the subcategories “terrorism,” “war and conflict,” “death and tragedy,” or “other.” There’s also “Drugs” and “Sex/Nudity” (with the subcategories “abusive,” “nudity,” or “other”). The system also provides the ad rater with an option for "other sensitive content"—if, say, someone is sharing extreme political views. ( AdAge recently reported that Google is now allowing clients to opt out from advertising alongside “sexually suggestive” and “sensational and shocking” content, as well as content containing “profanity and rough language.”) Some material doesn't always fit neatly into the provided categories, ad raters say. In those cases, raters label the material as "unrateable." One current rater described how he had to evaluate two Spanish-speaking people engaged in a rap battle. "I checked it as unrateable because of the foreign language," he told WIRED. "I also added a comment that said it seems like this is a video of people insulting each other in a foreign language, but I can't exactly tell if they are using profanity." (Judging from recent ad-rating job openings, one former rater said, Google seems to be prioritizing hiring bilingual raters. Workers can also check a box when a video is in a language they don’t understand.) YouTube’s Ad Problems Finally Blow Up in Google’s Face AI Isn’t Smart Enough (Yet) to Spot Horrific Facebook Videos Google Goes After Bad Ads and Bad Sites That Profit From Them The Laborers Who Keep Dick Pics and Beheadings Out of Your Facebook Feed Multiple ad raters say they have been asked to watch videos with shocking content. “The graphic stuff is far more graphic lately … someone trying to commit suicide with their dog in their truck,” one rater said. The person set the truck on fire, the rater said, then exited the truck and committed suicide with a shot to the head. In the online forums frequented by ad raters, anonymous posters said they had seen videos of violence against women, children, and animals. Several posters said they needed to take breaks after watching several such videos in a row. Ad raters said they don’t know how Google selects the videos they will watch—they only see the title and thumbnail of the video before they rate it, not a rationale. Other typical content in videos raters are tasked to watch include people talking videogames, politics, and conspiracy theories. Taken together, the scope of the work and nuance required in assessing videos shows Google still needs human help in dealing with YouTube’s ad problems. “We have many sources of information, but one of our most important sources is people like you,” Google tells raters in a document describing the purpose of their ad-rating work. But while only machine intelligence can grapple with YouTube’s scale, as company execs and representatives have stressed again and again , until Google’s machines—or anyone else’s—get smart enough to distinguish, say, truly offensive speech from other forms of expression on its own, such efforts will still need to rely on people. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “We have always relied on a combination of technology and human reviews to analyze content that has been flagged to us, because understanding context in video can be subjective,” says Chi Hea Cho, a spokesperson for Google. “Recently we added more people to accelerate the reviews. These reviews help train our algorithms so they keep improving over time.” The ads quality rater program started in 2004, two sources told WIRED. It was modeled after Google’s ( much talked-about ) search quality evaluation program, and initially served Google’s core ad initiatives: AdWords , which generates ads that correspond to search results and AdSense , which place ads on websites through Google. The original hiring agency for ad raters, ABE, paid ad raters $20 an hour. They could work full time and even overtime, one former rater said. In 2006, WorkForceLogic acquired ABE , after which raters say working conditions became less favorable. A company called ZeroChaos bought WorkForceLogic in 2012 and contracts with ad raters today. Ad rating work often attracts people who prefer more flexible working conditions, among them college graduates who have just entered the workforce, workers nearing retirement age, stay-at-home parents, and individuals with physical disabilities. Ad raters can work wherever and whenever they want, as long as they fulfill the 10-hour weekly minimum work requirement. Raters only need their own desktop computer and mobile device to work. The scope of the work and nuance required in assessing videos shows Google still needs human help. But the inherent instability of the job can take a toll on many workers. “Most of us love this job,” one ad rater tells WIRED, “but we have no chance of becoming permanent, full-time employees.” Most of the ad raters who spoke to WIRED were hired through ZeroChaos—just one of several agencies that provide temporary workers to tech companies. ZeroChaos hires ad raters on one-year contracts, and at least until very recently they could not stay on the job after two years of continuous work. Some workers believe this limit deprived the company of experienced raters best qualified to do the work. (In early April, during our reporting on this story, ZeroChaos notified ad raters that it was ending the two-year limit.) Ad raters do not get raises—they earn $15 per hour and can work a maximum of 29 hours a week. They get no paid time off. They can sign up for benefits if they work at least 25 hours per week, but they have no assurance they will have enough tasks to meet that threshold. Workers say they can find themselves dismissed suddenly, without warning and without a reason given—something multiple employees say has happened to them, including one after only a week on the job. The company notifies workers of termination with a perfunctory email message. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "Google strives to work with vendors that have a strong track record of good working conditions," says Cho. "When issues come to our attention, we alert these vendors about their employees’ concerns and work with them to address any issues. We will look into this matter further." ZeroChaos declined to comment. A lack of clear communication with Google itself compounds the feelings of job insecurity ad raters have, they say. They don’t meet anyone they work for in person—including during the job interview process—and Google gives raters only a generic Google email for the “Ads Evaluation Administrative team,” telling the raters to use it only for task-related issues. When raters email the address, they only receive an auto-response. “Because of the volume of reports received, administrators do not respond to individual problem reports: instead, we monitor incoming reports to detect system-wide problems as quickly as possible,” Google’s reply reads. “If you need an individualized response, or a specific action taken on your account, contact your contract administrator instead.” “The communication from Google was totally nonexistent,” one former rater said. “Google is legendary for not communicating.” “The people at the other end of this pipeline in Mountain View are like the wizard behind the curtain,” another former rater said. “We would like very much to communicate with them, be real colleagues, but no.” For its part, Google does inform raters that they’re doing important work, even if it doesn’t spell out exactly why. “We won’t always be able to tell you what [each] task is for, but it’s always something we consider important,” the company explains in orientation materials for ad raters. “You won’t often hear about the results of your work. In fact, it sometimes might seem like your work just flows into a black hole … Even though you don’t always see the impact, your work is very important, and many people at Google review it very, very closely.” Sometimes too closely for some workers’ comfort. Google incorporates already-reviewed content into ad raters’ assignments to gauge their performance. "These exams appear as normal tasks, and you will acquire them along with your regular work," an email to an ad rater from Google reads. "You will not be able to tell which tasks you are being tested on … We use exam scores to evaluate your performance. Very low scores may result in your assignment being ended." Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Embedding questions with known answers is a common practice in crowdsourcing research, according to Georgia Tech AI researcher Mark Riedl. The strategy is often used to determine whether a researcher should throw out data from an individual who might be clicking randomly, he explains, and it's often jokingly called the Turing Test among practitioners. But Riedl says he doesn't care for the Turing Test reference. "It perpetuates an attitude that crowd workers are machines when instead we need to recognize that crowd workers are humans, for whom we have an ethical and moral responsibility to design tasks that recognize the dignity of the worker," he says. To be sure, not all ad raters find fault with the issues raised by some of their fellow workers. The $15-per-hour rate is still above most cities’ minimum wages. One ad rater told me he was grateful for the opportunity ZeroChaos gave him. “[ZeroChaos] didn’t care about a criminal background when even McDonald’s turned me down,” the rater said. Multiple raters said they’d been close to homelessness or needing to go on food stamps when this job came along. But others say the flexibility often doesn’t end up working in their favor, even as they come to depend on this job. Working from home and choosing one’s own hours are perks. But according to a ZeroChaos FAQ, ad raters are prohibited from working for other companies at the same time. One former ad rater says she is now doing temp work for another company through ZeroChaos and would also like to resume doing ad rater work to help make ends meet, but can't because of the restriction. “If I could work jobs simultaneously, that would be great, a living wage,” she says. “Right now, I'm earning $40 a week more than I did on unemployment. That's not sustainable.” Big companies across the tech industry employ temporary workers to participate in repetitive tasks meant to train AI systems, according to multiple ad raters WIRED spoke with. One ad rater described a job several years ago rating Microsoft Bing search results, in which human evaluators were expected to go through as many as 80 pages of search results an hour. LinkedIn and Facebook employ humans for similar tasks, too, raters told me—LinkedIn for data annotation and Facebook for rating "sponsored posts" from fan pages. (Microsoft declined to comment, while LinkedIn could not confirm such a program. Facebook did not respond to a request for comment.) Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The overall job insecurity of temp work and widespread turnover unsettles current and former employees, who argue Google is losing the institutional knowledge possessed by workers who have spent more time on the job. “They’re wasting money by taking the time to train new people, then booting them out the door,” says one former ad rater. But churning through human ad raters may just reflect best practices for making AI smarter. Artificial intelligence researchers and industry experts say a regular rotation of human trainers inputting data is better for training AI. "AI needs many perspectives, especially in areas like offensive content," says Jana Eggers, CEO of AI startup Nara Logics. Even the Supreme Court could not describe obscenity, she points out, citing the “I know it when I see it” threshold test. "Giving 'the machine' more eyes to see is going to be a better result." But while AI researchers agree in general that poor human morale doesn’t necessarily cause poor machine learning, there may be more subtle effects that stem from one's work environment and experiences. "One often hears the perspective that getting large amounts of diverse inputs is the way to go for training AI models," says Bart Selman, a Cornell University AI professor. "This is often a good general guideline, but when it comes to ethical judgments it is also known that there are significant ingrained biases in most groups." For example, Selman says, the perception that men are better at certain types of jobs than women, and vice versa. "So, if your train your AI hiring model on the perceptions of a regular group of opinions, or past hiring decisions, you will get the hidden biases present in the general population." And if it turns out you’re training your AI mainly on the perceptions of anxious temp workers, they could wind up embedding their own distinct biases in those systems. "You would not want to train an AI ethics module by having it observe what regular people do in everyday life," Selman says. "You want to get input from people that have thought about potential biases and ethical issues more carefully." Googlers at the company’s sprawling Mountain View headquarters enjoy a picturesque campus, free gourmet cafeteria food, and rec room games like pool and foosball. That’s a far cry from the life of a typical ad rater. These days, working for the world’s most valuable tech companies can mean luxurious perks and huge paydays. It can also mean toiling away as a temp worker at rote tasks, training these companies' machines to do the same work. Have any secrets you want to share? Use WIRED SecureDrop. Topics Advertising artificial intelligence Google YouTube Paresh Dave K.G. Orphanides Will Knight Niamh Rowe Reece Rogers Vittoria Elliott Vittoria Elliott David Gilbert Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. 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1,929	2,017	"Inside Facebook’s AI Machine \| WIRED"	"https://www.wired.com/2017/02/inside-facebooks-ai-machine"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Steven Levy Backchannel Inside Facebook’s AI Machine Save this story Save Save this story Save When asked to head Facebook’s Applied Machine Learning group — to supercharge the world’s biggest social network with an AI makeover — Joaquin Quiñonero Candela hesitated. It was not that the Spanish-born scientist, a self-described “machine learning (ML) person,” hadn’t already witnessed how AI could help Facebook. Since joining the company in 2012, he had overseen a transformation of the company’s ad operation, using an ML approach to make sponsored posts more relevant and effective. Significantly, he did this in a way that empowered engineers in his group to use AI even if they weren’t trained to do so, making the ad division richer overall in machine learning skills. But he wasn’t sure the same magic would take hold in the larger arena of Facebook, where billions of people-to-people connections depend on fuzzier values than the hard data that measures ads. “I wanted to be convinced that there was going to be value in it,” he says of the promotion. Despite his doubts, Candela took the post. And now, after barely two years, his hesitation seems almost absurd. How absurd? Last month, Candela addressed an audience of engineers at a New York City conference. “I’m going to make a strong statement,” he warned them. “Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger, you may not realize it, but your experiences are being powered by AI.” Joaquin Candela, Director of Engineering for Applied Machine Learning at Facebook. Stephen Lam Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Last November I went to Facebook’s mammoth headquarters in Menlo Park to interview Candela and some of his team, so that I could see how AI suddenly became Facebook’s oxygen. To date, much of the attention around Facebook’s presence in the field has been focused on its world-class Facebook Artificial Intelligence Research group (FAIR), led by renowned neural net expert Yann LeCun. FAIR, along with competitors at Google, Microsoft, Baidu, Amazon, and Apple (now that the secretive company is allowing its scientists to publish), is one of the preferred destinations for coveted grads of elite AI programs. It’s one of the top producers of breakthroughs in the brain-inspired digital neural networks behind recent improvements in the way computers see, hear, and even converse. But Candela’s Applied Machine Learning group (AML) is charged with integrating the research of FAIR and other outposts into Facebook’s actual products—and, perhaps more importantly, empowering all of the company’s engineers to integrate machine learning into their work. Because Facebook can’t exist without AI, it needs all its engineers to build with it. My visit occurs two days after the presidential election and one day after CEO Mark Zuckerberg blithely remarked that “it’s crazy” to think that Facebook’s circulation of fake news helped elect Donald Trump. The comment would turn out be the equivalent of driving a fuel tanker into a growing fire of outrage over Facebook’s alleged complicity in the orgy of misinformation that plagued its News Feed in the last year. Though much of the controversy is beyond Candela’s pay grade, he knows that ultimately Facebook’s response to the fake news crisis will rely on machine learning efforts in which his own team will have a part. But to the relief of the PR person sitting in on our interview, Candela wants to show me something else—a demo that embodies the work of his group. To my surprise, it’s something that performs a relatively frivolous trick: It redraws a photo or streams a video in the style of an art masterpiece by a distinctive painter. In fact, it’s reminiscent of the kind of digital stunt you’d see on Snapchat, and the idea of transmogrifying photos into Picasso’s cubism has already been accomplished. “The technology behind this is called neural style transfer,” he explains. “It’s a big neural net that gets trained to repaint an original photograph using a particular style.” He pulls out his phone and snaps a photo. A tap and a swipe later, it turns into a recognizable offshoot of Van Gogh’s “The Starry Night.” More impressively, it can render a video in a given style as it streams. But what’s really different, he says, is something I can’t see: Facebook has built its neural net so it will work on the phone itself. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg That isn’t novel, either — Apple has previously bragged that it does some neural computation on the iPhone. But the task was much harder for Facebook because, well, it doesn’t control the hardware. Candela says his team could execute this trick because the group’s work is cumulative — each project makes it easier to build another, and every project is constructed so that future engineers can build similar products with less training required —so stuff like this can be built quickly. “It took eight weeks from us to start working on this to the moment we had a public test, which is pretty crazy,” he says. (L-R) Joaquin Candela, Director of Engineering for Applied Machine Learning; Manohar Paluri, Applied Computer Vision Team Lead; Rita Aquino, Technical Product Manager; and Rajen Subba, Engineering Manager. Stephen Lam The other secret in pulling off a task like this, he says, is collaboration—a mainstay of Facebook culture. In this case, easy access to other groups in Facebook — specifically the mobile team intimately familiar with iPhone hardware — led to the jump from rendering images in Facebook’s data centers to performing the work on the phone itself. The benefits won’t only come from making movies of your friends and relatives looking like the woman in “The Scream.” It’s a step toward making all of Facebook more powerful. In the short term, this allows for quicker responses in interpreting languages and understanding text. Longer term, it could enable real-time analysis of what you see and say. “We’re talking about seconds, less than seconds — this has to be real time,” he says. “ We’re the social network. If I’m going to make predictions about people’s feedback on a piece of content, [my system] needs to react immediately, right?” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Candela takes another look at the Van Gogh-ified version of the selfie he’s just shot, not bothering to mask his pride. “By running complex neural nets on the phone, you’re putting AI in the hands of everybody,” he says. “That does not happen by chance. It’s part of how we’ve actually democratized AI inside the company. “It’s been a long journey,” he adds. Candela was born in Spain. His family moved to Morocco when he was three, and he attended French language schools there. Though his grades were equally high in science and humanities, he decided to attend college in Madrid, ideally studying the hardest subject he could think of: telecommunications engineering, which not only required a mastery of physical stuff like antennas and amplifiers, but also an understanding of data, which was “really cool.” He fell under the spell of a professor who proselytized adaptive systems. Candela built a system that used intelligent filters to improve the signal of roaming phones; he describes it now as “a baby neural net.” His fascination with training algorithms, rather than simply churning out code, was further fueled by a semester he spent in Denmark in 2000, where he met Carl Rasmussen , a machine learning professor who had studied with the legendary Geoff Hinton in Toronto—the ultimate cool kid credential in machine learning. Ready for graduation, Candela was about to enter a leadership program at Procter & Gamble when Rasmussen invited him to study for a PhD. He chose machine learning. In 2007, he went to work at Microsoft Research’s lab in Cambridge, England. Soon after he arrived, he learned about a company-wide competition: Microsoft was about to launch Bing, but needed improvement in a key component of search ads — accurately predicting when a user would click on an ad. The company decided to open an internal competition. The winning team’s solution would be tested to see if it was launch-worthy, and the team members would get a free trip to Hawaii. Nineteen teams competed, and Candela’s tied for the winner. He got the free trip, but felt cheated when Microsoft stalled on the larger prize — the test that would determine if his work could be shipped. What happened next shows Candela’s resolve. He embarked on a “crazy crusade” to make the company give him a chance. He gave over 50 internal talks. He built a simulator to show his algorithm’s superiority. He stalked the VP who could make the decision, positioning himself next to the guy in buffet lines and synching his bathroom trips to hype his system from an adjoining urinal; he moved into an unused space near the executive, and popped into the man’s office unannounced, arguing that a promise was a promise, and his algorithm was better. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Candela’s algorithm shipped with Bing in 2009. In early 2012, Candela visited a friend who worked at Facebook and spent a Friday on its Menlo Park campus. He was blown away to discover that at this company, people didn’t have to beg for permission to get their work tested. They just did it. He interviewed at Facebook that next Monday. By the end of the week he had an offer. Joining Facebook’s ad team, Candela’s task was to lead a group that would show more relevant ads. Though the system at the time did use machine learning, “the models we were using were not very advanced. They were pretty simple,” says Candela. An interior view of Facebook Building 20. Stephen Lam Another engineer who had joined Facebook at the same time as Candela (they attended the new employee “code boot camp” together) was Hussein Mehanna, who was similarly surprised at the lack of the company’s progress in building AI into its system. “When I was outside of Facebook and saw the quality of the product, I thought all of this was already in shape, but apparently it wasn’t,” Mehanna says. “Within a couple of weeks I told Joaquin that what’s really missing at Facebook is a proper, world-class machine learning platform. We had machines but we didn’t have the right software that would could help the machines learn as much as possible from the data.” (Mehanna, who is now Facebook’s director of core machine learning, is also a Microsoft veteran — as are several other engineers interviewed for this story. Coincidence?) Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg By “machine learning platform,” Mehanna was referring to the adoption of the paradigm that has taken AI from its barren “winter” of the last century (when early promises of “thinking machines” fell flat) to its more recent blossoming after the adoption of models roughly based on the way the brain behaves. In the case of ads, Facebook needs its system to do something that no human is capable of: Make an instant (and accurate!) prediction of how many people will click on a given ad. Candela and his team set out to create a new system based on the procedures of machine learning. And because the team wanted to build the system as a platform, accessible to all the engineers working in the division, they did it in a way where the modeling and training could be generalized and replicable. One huge factor in building machine learning systems is getting quality data—the more the better. Fortunately, this is one of Facebook’s biggest assets: When you have over a billion people interacting with your product every day, you collect a lot of data for your training sets, and you get endless examples of user behavior once you start testing. This allowed the ads team to go from shipping a new model every few weeks to shipping several models every week. And because this was going to be a platform — something that others would use internally to build their own products — Candela made sure to do his work in a way where multiple teams were involved. It’s a neat, three-step process. “You focus on performance, then focus on utility, and then build a community,” he says. Candela’s ad team has proven how transformative machine learning could be at Facebook. “We became incredibly successful at predicting clicks, likes, conversions, and so on,” he says. The idea of extending that approach to the larger service was natural. In fact, FAIR leader LeCun had already been arguing for a companion group devoted to applying AI to products — specifically in a way that would spread the ML methodology more widely within the company. “I really pushed for it to exist, because you need organizations with highly talented engineers who are not directly focused on products, but on basic technology that can be used by a lot of product groups,” LeCun says. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Candela became director of the new AML team in October 2015 (for a while, because of his wariness, he kept his post in the ads division and shuttled between the two). He maintains a close relationship with FAIR, which is based in New York City, Paris, and Menlo Park, and where its researchers literally sit next to AML engineers. The way the collaboration works can be illustrated by a product in progress that provides spoken descriptions of photos people post to Facebook. In the past few years, it has become a fairly standard AI practice to train a system to identify objects in a scene or make a general conclusion, like whether the photo was taken indoors or outdoors. But recently, FAIR’s scientists have found ways to train neural nets to outline virtually every interesting object in the image and then figure out from its position and relation to the other objects what the photo is all about—actually analyzing poses to discern that in a given picture people are hugging, or someone is riding a horse. “We showed this to the people at AML,” says LeCun, “and they thought about it for a few moments and said, ‘You know, there’s this situation where that would be really useful.’” What emerged was a prototype for a feature that could let blind or visually impaired people put their fingers over an image and have their phones read them a description of what’s happening. “We talk all the time,” says Candela of his sister team. “The bigger context is that to go from science to project, you need the glue, right? We are the glue.” Candela breaks down the applications of AI in four areas: vision, language, speech, and camera effects. All of those, he says, will lead to a “content understanding engine.” By figuring out how to actually know what content means, Facebook intends to detect subtle intent from comments, extract nuance from the spoken word, identify faces of your friends that fleetingly appear in videos, and interpret your expressions and map them onto avatars in virtual reality sessions. “We are working on the generalization of AI,” says Candela. “With the explosion of content we need to understand and analyze, our ability to generate labels that tells what things can’t keep up.” The solution lies in building generalized systems where work on one project can accrue to the benefit of other teams working on related projects. Says Candela, “If I can build algorithms where I can transfer knowledge from one task to another, that’s awesome, right?” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg That transfer can make a huge difference in how quickly Facebook ships products. Take Instagram. Since its beginning, the photo service displayed user photos in reverse chronological order. But early in 2016, it decided to use algorithms to rank photos by relevance. The good news was that because AML had already implemented machine learning in products like the News Feed, “they didn’t have to start from scratch,” says Candela. “They had one or two ML-savvy engineers contact some of the several dozen teams that are running ranking applications of one kind or another. Then you can clone that workflow and talk to the person if you have questions.” As a result, Instagram was able to implement this epochal shift in only a few months. The AML team is always on the prowl for use cases where its neural net prowess can be combined with a collection of different teams to produce a unique feature that works at “Facebook scale.” “We’re using machine learning techniques to build our core capabilities and delight our users,”says Tommer Leyvand, a lead engineer of AML’s perception team. (He came from…wait for it…Microsoft.) Rita Aquino, Technical Product Manager at Facebook. Stephen Lam Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg An example is a recent feature called Social Recommendations. About a year ago, an AML engineer and a product manager for Facebook’s sharing team were talking about the high engagement that occurs when people ask their friends for recommendations about local restaurants or services. “The issue is, how do you surface that to a user?” says Rita Aquino, a product manager on AML’s natural language team. (She used to be a PM at…oh, forget it.) The sharing team had been trying to do that by word matching certain phrases associated with recommendation requests. “That’s not necessarily very precise and scalable, when you have a billion posts per day,” Aquino says. By training neural nets and then testing the models with live behavior, the team was able to detect very subtle linguistic differences so it could accurately detect when someone was asking where to eat or buy shoes in a given area. That triggers a request that appears on the News Feed of appropriate contacts. The next step, also powered by machine learning, figures out when someone supplies a plausible recommendation, and actually shows the location of the business or restaurant on a map in the user’s News Feed. Aquino says in the year and half she has been at Facebook, AI has gone from being a fairly rare component in products to something now baked in from conception. “People expect the product they interact with to be smarter,” she says. “Teams see products like social recommendations, see our code, and go — ‘How do we do that?’ You don’t have to be a machine learning expert to try it out for your group’s experience.” In the case of natural language processing, the team built a system that other teams can easily access, called Deep Text. It helps power the ML technology behind Facebook’s translation feature, which is used for over four billion posts a day. For images and video, the AML team has built a machine learning vision platform called Lumos. It originated with Manohar Paluri, then an intern at FAIR who was working on a grand machine learning vision he calls the visual cortex of Facebook — a means of processing and understanding all the images and videos posted on Facebook. At a 2014 hackathon, Paluri and colleague Nikhil Johri cooked up a prototype in a day and a half and showed the results to an enthusiastic Zuckerberg and Facebook COO Sheryl Sandberg. When Candela began AML, Paluri joined him to lead the computer vision team and to build out Lumos to help all of Facebook’s engineers (including those at Instagram, Messenger, WhatsApp, and Oculus) make use of the visual cortex. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg With Lumos, “anybody in the company can use features from these various neural networks and build models for their specific scenario and see how it works,” says Paluri, who holds joint positions in AML and FAIR. “And then they can have a human in the loop correct the system, and retrain it, and push it, without anybody in the [AML] team being involved.” Paluri gives me a quick demo. He fires up Lumos on his laptop and we undertake a sample task: refining the neural net’s ability to identify helicopters. A page packed with images — if we keep scrolling, there would be 5,000 — appears on the screen, full of pictures of helicopters and things that aren’t quite helicopters. (One is a toy helicopter; others are objects in the sky at helicopter-ish angles.) For these datasets, Facebook uses publicly posted images from its properties—those limited to friends or other groups are off limits. Even though I’m totally not an engineer, let alone an AI-adept, it’s easy to click on negative examples to “train an image classifier for helicopters,” as the jargon would have it. Eventually, this “classifying” step—known as supervised learning—may become automated, as the company pursues an ML holy grail known as “unsupervised learning,” where the neural nets are able to figure out for themselves what stuff is in all those images. Paluri says the company is making progress. “Our goal is to reduce the number of (human) annotations by 100 times in the next year,” he says. In the long term, Facebook sees the visual cortex merging with the natural language platform for the generalized content understanding engine that Candela spoke about. “No doubt we will end up combining them together,” says Paluri. “Then we’ll just make it…cortex.” Ultimately, Facebook hopes that the core principles it uses for its advances will spread even outside the company, through published papers and such, so that its democratizing methodology will spread machine learning more widely. “Instead of spending ages and ages trying to build an intelligent application, you can build applications far faster,” says Mehanna. “Imagine the impact of this on medicine, safety, and transportation. I think building applications in those domains is going to be faster by a hundred-x magnitude.” Manohar Paluri, Applied Computer Vision Team Lead at Facebook, at Building 20 in Menlo Park, Calif. on Monday, Feb. 6, 2017. Stephen Lam Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Though AML is deeply involved in the epic process of helping Facebook’s products see, interpret, and even speak, CEO Zuckerberg also sees it as critical to his vision of Facebook as a company working for social good. In Zuckerberg’s 5,700-word manifesto about building communities, the CEO invoked the words “artificial intelligence” or “AI” seven times, all in the context of how machine learning and other techniques will help keep communities safe and well informed. Fulfilling those goals won’t be easy, for the same reasons that Candela first worried about taking the AML job. Even machine learning can’t resolve all those people problems that come when you are trying to be the main source of information and personal connections for a couple billion users. That’s why Facebook is constantly fiddling with the algorithms that determine what users see in their News Feeds—how do you train a system to deliver the optimal mix when you’re not really sure that that is? “I think this is almost an unsolvable problem,” says Candela. “Us showing news stories at random means you’re wasting most of your time, right? Us only showing news stories from one friend, winner takes all. You could end up in this round-and-round discussion forever where neither of the two extremes is optimal. We try to bake in some explorations.” Facebook will keep trying to solve this with AI, which has become the company’s inevitable hammer to drive in every nail. “There’s a bunch of action research in machine learning and in AI in optimizing the right level of exploration,” Candela says, sounding hopeful. Naturally, when Facebook found itself named a culprit in the fake news blame-athon, it called on its AI teams to quickly purge journalistic hoaxes from the service. It was an unusual all-hands effort, including even the long-horizon FAIR team, which was was tapped almost “as consultants,” says LeCun. As it turns out, FAIR’s efforts had already produced a tool to help with the problem: a model called World2Vec (“vec” being a shorthand for the technical term, vectors). World2Vec adds a sort of memory capability to neural nets, and helps Facebook tag every piece of content with information, like its origin and who has shared it. (This is not be be confused, though I originally was, with a Google innovation called Word2Vec. ) With that information, Facebook can understand the sharing patterns that characterize fake news, and potentially use its machine learning tactics to root out the hoaxes. “It turns out that identifying fake news isn’t so different than finding the best pages people want to see,” says LeCun. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The preexisting platforms that Candela’s team built made it possible for Facebook to launch those vetting products sooner than they could have done otherwise. How well they actually perform remains to be seen; Candela says it’s too soon to share metrics on how well the company has managed to reduce fake news by its algorithmic referees. But whether or not those new measures work, the quandary itself raises the question of whether an algorithmic approach to solving problems — even one enhanced by machine learning — might inevitably have unintended and even harmful consequences. Certainly some people contend that this happened in 2016. Candela rejects that argument. “I think that we’ve made the world a much better place,” he says, and offers to tell a story. The day before our interview, Candela made a call to a Facebook connection he had met only once—a father of one of his friends. He had seen that person posting pro-Trump stories, and was perplexed by their thinking. Then Candela realized that his job is to make decisions based on data, and he was missing important information. So he messaged the person and asked for a conversation. The contact agreed, and they spoke by phone. “It didn’t change reality for me, but made me look at things in a very, very different way,” says Candela. “In a non-Facebook world I never would have had that connection.” In other words, though AI is essential — even existential — for Facebook, it’s not the only answer. “The challenge is that AI is really in its infancy still,” says Candela. “We’re only getting started.” Creative Art Direction: Redindhi Studio Photography by: Stephen Lam [ How Google is Remaking Itself as a “Machine Learning First” Company If you want to build artificial intelligence into every product, you better retrain your army of coders. Check.backchannel.com ](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70 "https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70")[ You Too Can Become a Machine Learning Rock Star! No PhD Necessary. Neural net startup Bonsai launches AI for dummies.backchannel.com ](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b "https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b")[ An Exclusive Look at How AI and Machine Learning Work at Apple The iBrain is here — and it’s already inside your phone.backchannel.com ](https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b "https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b") Editor at Large X Topics Backchannel artificial intelligence Facebook Steven Levy Andy Greenberg Angela Watercutter Brandi Collins-Dexter Lauren Smiley Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,930	2,017	"Upworthy's Quest to Engineer Optimism for an Anxious Age \| WIRED"	"https://www.wired.com/2017/05/upworthys-quest-engineer-optimism-anxious-age"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Zachary Karabell Business Upworthy's Quest to Engineer Optimism for an Anxious Age Getty Images Save this story Save Save this story Save The world finds itself in an age saturated with anxiety—at least, that’s the sense created by the daily deluge of news portraying a grim present of economic hardship, global tensions, terrorism, and political upheaval. The five-year-old site Upworthy doesn’t want you to see the world that way. At one time, if Upworthy was known at all, it wasn’t for its mission, but for its attention-gathering headlines. “You wont believe what happened when this couple started saying nice things to each other.” Those were widely derided as “clickbait.” But they were effective. Upworthy figured out ahead of most more established media rivals that a shareable story equals a seen story. Now it’s chasing the battered but not-yet-extinguished promise of an optimistic take on the world by marrying a lexicon of idealism to an almost metronomic pursuit of substantive clicks. In March of 2012, Eli Pariser—one of the leaders of the activist group MoveOn—and Peter Koechley—also of MoveOn and an editor at The Onion—launched Upworthy with several million dollars of seed money and a surfeit of hope. It was and is a bold attempt at reframing what constitutes news. In a world of proliferating information that travels like quicksilver through the virtual ether, the media adage “if it bleeds, it leads” has never been more relevant. Fear and anger are the currency of the media realm. Upworthy seeks to upend that formula and focus instead not on what is going wrong but on what might go right. Let it be stipulated that in a time beset by deep, chronic pessimism about the fate of the world, an endeavor founded on the premise of positive social engagement (which is how Upworthy describes its mission) can sound achingly naïve. I cannot count the number of times that I have advocated a more optimistic way of looking at the world only to be ineluctably forced into a defensive posture by the pushback of others, as if championing a more constructive view constitutes an unconscionable rejection of risks and suffering that are everywhere evident. Upworthy adamantly rejects that, and insists that stories “can make the world a better place” and engage people in a way that makes them want to do something instead of tuning out. Fear and anger are the currency of the media realm. Upworthy seeks to upend that formula. On the numbers, Upworthy has 11 million subscribers, 20 million unique visitors to its website, and more important, substantial community engagement through its main distribution platform, Facebook. For those of you who think Upworthy has faded, Facebook’s own research (at least according to Upworthy) demonstrates that the site and its stories have some of the highest community engagement of any Facebook page, behind Fox News but ahead of CNN, the Huffington Post and Buzzfeed. The exact methodology of “engagement” is a bit opaque, but include metrics such as time spent reading, number of shares, comments and links. A recent glance at the home page shows a mix of soft and hard news, with “ Bill Nye’s new show is opionated, unapologetic and exactly what we need ” along with “ Stigma against undocumented immigrant hurts everyone seeking a better life in the US ”, “ What Serena Williams wrote to her baby on Instagram and why it matters ,” and “ Things get heated as New Orleans dismantles Confederate monuments. ” As a scan of Upworthy headlines these days suggests, its version of optimism doesn’t seem to have strayed far from its founders’ roots in liberal politics. Upworthy says it does not define its mission in left-right, liberal-conservative terms, and Pariser says the site’s audience is surprisingly diverse in terms of politics and geography—an expected clump of progressives but more than a handful of Trump supporters and centrists and “folks who aren’t really tuned in to politics.” Content-wise, however, Upworthy clearly hasn’t discovered a magic formula for transcending the intractable pull of partisanship online. Its experiment seems to be more one of tone: positive encouragement rather than inflammatory antagonism. In addition to his work at MoveOn, Pariser also authored a book called The Filter Bubble in 2012, which anticipated the great dangers of a world where news is disseminated primarily by internet platforms: People read only news and stories that accord with their views. At the same time, Upworthy depends on those same platforms, relying on advanced techniques of audience engagement to get more eyeballs. The result is simultaneously uplifting with a slight uneasy feeling that you are being manipulated, a sense that has caused problems for the company in the past. Three years ago, Upworthy found itself whipsawed by Facebook’s changing algorithms , with hits skyrocketing above 80 million, then plummeting in half in the subsequent months when Facebook changed the algorithm it used to place and disseminate content. It was around the same time that Upworthy came under fire for “clickbait” stories that seemed designed to get traffic rather than foster substantive discussions. Headlines like “This Amazing Kid Just Died: What He Left Behind Is Wundtacular,” were held up as Exhibit A along with equivalent stories on sites such as Buzzfeed that seemed more interested in generating quick attention. Upworthy’s vulnerability to Facebook’s mercurial algorithms only seemed to solidify the impression that the site was good at buzz, less good at substance. Hate the News? Wikipedia’s Co-Founder Wants You to Edit It Inside the Buzz-Fueled Media Startups Battling for Your Attention How to Stay Focused in a Garbage-Fire News Cycle Still, one person’s clickbait is another’s definition of a catchy headline that draws readers into a good story. Editors have been doing that for a century, and some of the ire directed at Upworthy was undoubtedly a mix of scorn for the naked idealism and some legitimate critiques of a site still finding its voice. In the years since, Upworthy has refined its methods and moved toward more of its own content rather than repurposing others. It has placed a heavy emphasis on video and recently merged with the parent company of equally optimistic GOOD magazine. Upworthy's current editorial director is Amy O’Leary, who came from the New York Times a few years ago. She emphasizes that they aren’t looking for “feel good” stories as much as stories that “have weight and influence, that have a message embedded in the story that leads to action and influence.” Facebook remains a powerful distribution platform, and dependency on that doesn’t alarm O’Leary much. “There are strong advantages to having a powerful Facebook audience,” she explained to me. “They want to create a place where people want to spend lots of time, and Upworthy tries to create stories that people want to spend time with, and so long as those are aligned, I see a lot of opportunity and not much threat.” Given Pariser’s history with activism and his insights into how information is parsed and segregated, the Upworthy team is intensively focused on metrics, on gauging what works in a narrative and then tweaking and testing different story and video structures to gain more engagement. They can and do shift headlines, words, story flows and narrative construction to see who reads what and for how long, and then try to codify that into formulas of what works and what doesn’t. Whether you find the idea of a control room replete with graphs and metrics to tailor messages creepy or encouraging depends on your sense of what media means in an era when far more “user behavior” can be measured. Consumer companies do that constantly, as do politicians. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The question for Upworthy going forward is not just can it survive as a media company but does it stay a media company (if it can survive economically) and seek ever more platforms and partnerships? Does it seek to harness its audience for political and social activism, or give those audiences greater tools to put their passions to work? You could imagine multiple pathways with Upworthy combining with groups like Change.org or DonorsChose or a political party. You could also imagine Upworthy becoming the go-to home for stories with a purpose as surely as Breitbart is home to American nationalism. Finally, there is the unanswered question of whether it will become trapped in its own “filter bubble” of urban, educated readers or find a path to significantly broaden its appeal. And of course, it isn’t yet clear that optimism, or at least Upworthy’s brand of it, has a more than a niche market. Fear and anger have a mass market, but hope and a vision for a better future, who knows? Brands and companies like to affiliate themselves with an uplifting ethos, but ultimately their goal is to sell products. Upworthy’s goal is to propagate ideas that matter and make enough money to continue doing so at ever-greater scale. Optimism is a needed lubricant for positive social change; Upworthy is one test if it is also an idea that sells. For now, Upworthy appears to be reaching a wide enough audience to attract investors and make some coin. Whether it is yet shaping the discussion is another question, and given the plethora of dark pessimism, it has a steep road before its ripples make even modest waves on the murky pond of contemporary journalism. Clearly, it has tapped a chord in our culture that is tuning out the cacophony of impending crisis and is instead yearning to hear about what is working and and might. Like Upworthy or cringe in the face of it, we need that chord to grow and thrive if our worst fears and darkest instincts are not to dominate. Topics Advertising Media Khari Johnson Will Bedingfield Vittoria Elliott Vittoria Elliott David Gilbert Matt Burgess Will Knight Paresh Dave Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,931	2,016	"Facebook Is Teaching Chatbots to Talk With Help From Facebook \| WIRED"	"https://www.wired.com/2016/06/facebook-teaching-chatbots-talk-help-facebook"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Cade Metz Business Facebook Is Teaching Chatbots to Talk With Help From Facebook Getty Images Save this story Save Save this story Save First Microsoft. Then Facebook. And now Google. As is so often the case, the giants of the Internet are chasing the same sparkly vision of the future: chatbots. In the coming months and years, these companies promise , you'll chat with Internet services in much the same way you now chat with friends and family. Bots will instantly answer questions, respond to requests, and even anticipate your needs. While chatting with some some old college pals about an upcoming reunion, you'll ask an OpenTable bot for restaurant recommendations. Without opening a separate app, you'll book a hotel through Travelocity. Google’s New Allo Messaging App Gets Its Edge From AI Google Has Open Sourced SyntaxNet, Its AI for Understanding Language Facebook Open Sources Its AI Hardware as It Races Google But a major challenge remains: building chatbots that can actually chat. Machines can mimic conversation in some ways, but they're still a long way from really grasping the way humans talk. Late last month, in an effort to advance the progress of such AI---and score PR points against its rivals---Google open sourced one of the tools it uses for natural language understanding. (If you share, you get more people pushing the state-of-the-art). And today, not to be outdone, Facebook unveiled an important part of its own underlying technology, a natural language engine it calls DeepText. Facebook is not yet open sourcing this technology. And the company is only beginning to use DeepText with its own services. But as described by Facebook, DeepText shows how the giants of the Internet hope to accelerate the progress of natural language understanding in the months and years to come. In building these systems, they aim to rely far less on humans and far more on data---enormous troves of online data. Both Google and Facebook are now using deep neural networks to advance their natural language ambitions. Deep neural nets have already proven so effective for so many other online tasks , such as recognizing faces in photos or identifying commands spoken into smartphones, and the hope is that these networks of software and hardware, which learn discrete tasks by analyzing vast amounts of data, will prove just as effective in learning to understand and respond to language in a natural way. Facebook Google's newly open sourced system, called SyntaxNet, uses neural nets to understand the grammatical logic of a given sentence. Much as a neural net can learn to recognize a cat by analyzing millions of cat photos, it can learn to understand grammar---nouns, verbs, how a verb relates to the object, and more---by analyzing millions of sentences. This approach, called syntactic parsing, is effective, but it's not without limitations. Humans must carefully tag those millions of example sentences, identifying each part of speech and how it relates to the rest before SyntaxNet can learn from the data. And even if a machine learns to understand the grammar of a sentence, it must go significantly further to understand the complete meaning of a conversation. But Facebook researchers say they're already pushing the state-of-the-art into new territory. "[DeepText] helps us compensate for the lack of labeled data sets," says Facebook director of engineering Hussein Mehanna. "It comes with a massive amount of structure. It can learn in an unsupervised manner." In other words, Facebook's system relies more on math than grammatical exactitude. Facebook's system relies more on math than grammatical exactitude. "What they're saying is they didn't teach the neural network anything about the structure of language," explains Chris Nicholson, founder of deep learning startup Skymind, says of Facebook's work, which was previously discussed in a handful of public research papers. This is important, he adds, because it can make for a more flexible system---a system that can readily expand to so many different scenarios. Facebook's system can learn French or Spanish the same way it could English---by breaking it down to mere math. According to Mehanna, DeepText already works with 20 different languages. In the past, researchers built natural language engines using carefully coded rules---an approach that's difficult and time-consuming. That's how Apple built Siri. By building systems that learn on their own, companies like Google and Facebook are seeking to build systems that can grow and get smarter without as much human intervention. But we're not quite there yet. Facebook's methods are still in the early stages, and not everyone is convinced they're as effective as Facebook says they are. Noah Smith, a University of Washington computer scientist who specializes in natural language understanding, says Facebook's system is far from the only effort to reach such understanding through unlabeled data, and based on a recent Facebook research paper , Smith says, he doesn't find the company's approach especially exciting. But this is certainly an area where he and many others believe research will go. Mehanna says Facebook will publish newer research related to DeepText this summer. And he says the company is beginning to test the tool as a way of powering chatbots inside Facebook Messenger. As he explains it, the system can help recognize, during an ordinary chat with friends and family, when you're looking for a taxi ride (see graphic, left). And there's good reason to believe Facebook might have an edge here: data. In order to learn from natural language, you need enormous amounts of natural language---in digital form. Not so long ago, that was hard to come by. But Facebook has it droves---millions of real conversations that play out on its social network day after day. According to Mehanna, people create 400,000 new posts on the site with each passing minute, and each day, they post about 80 million comments on those posts. Yes, since Facebook is training DeepText on data it culls from its own site, it's hard for outside researchers to verify the company's claims of natural language proficiency. But this data is also a uniquely powerful thing. Right now, almost all the chatter on Facebook is humans talking to humans. But the machines are listening and learning, and one day, we may be talking with them too. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Senior Writer X Topics artificial intelligence bots Enterprise Facebook Khari Johnson Morgan Meaker Reece Rogers Gregory Barber Caitlin Harrington Nelson C.J. Peter Guest Andy Greenberg Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,932	2,022	"Destructive Hacks Against Ukraine Echo Its Last Cyberwar \| WIRED"	"https://www.wired.com/story/russia-ukraine-destructive-cyberattacks-ransomware-data-wiper"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Andy Greenberg Security Destructive Hacks Against Ukraine Echo Its Last Cyberwar Microsoft warned that the number of victims may still grow. Photograph: Mykola Tys/Getty Images Save this story Save Save this story Save For weeks, the cybersecurity world has braced for destructive hacking that might accompany or presage a Russian invasion of Ukraine. Now, the first wave of those attacks appear to have arrived. While so far on a small scale, the campaign uses techniques that hint at a rerun of Russia's massively disruptive campaign of cyberwar that paralyzed Ukraine's government and critical infrastructure in years past. Data-destroying malware, posing as ransomware, has hit computers within Ukrainian government agencies and related organizations, security researchers at Microsoft said Saturday night. The victims include an IT firm that manages a collection of websites, the same ones that hackers defaced with an anti-Ukrainian message early on Friday. But Microsoft also warned that the number of victims may still grow as the wiper malware is discovered on more networks. Viktor Zhora, a senior official at Ukraine's cybersecurity agency, known as the State Services for Special Communication and Information Protection, or SSSCIP, says that he first began hearing about the ransomware messages on Friday. Administrators found PCs locked and displaying a message demanding $10,000 in bitcoin, but the machines' hard drives were irreversibly corrupted when an admin rebooted them. He says SSSCIP has only found the malware on a handful of machines, but also that Microsoft warned the Ukrainians it had evidence the malware had infected dozens of systems. As of Sunday morning ET, one appears to have attempted to pay the ransom in full. "We're trying to see if this is linked to a larger attack," says Zhora. "This could be a first phase, part of more serious things that could happen in the near future. That’s why we’re very worried." Microsoft warns that when a PC infected with the fake ransomware is rebooted, the malware overwrites the computer's master boot record, or MBR, information on the hard drive that tells a computer how to load its operating system. Then it runs a file corruption program that overwrites a long list of file types in certain directories. Those destructive techniques are unusual for ransomware, Microsoft's blog post notes, given that they're not easily reversible if a victim pays a ransom. Neither the malware nor the ransom message appears customized for each victim in this campaign, suggesting the hackers had no intention of tracking victims or unlocking the machines of those who pay. Both of the malware's destructive techniques, as well as its fake ransomware message, carry eerie reminders of data-wiping cyberattacks Russia carried out against Ukrainian systems from 2015 to 2017 , sometimes with devastating results. In the 2015 and 2016 waves of those attacks, a group of hackers known as Sandworm , later identified as part of Russia's GRU military intelligence agency , used malware similar to the kind Microsoft has identified to wipe hundreds of PCs inside Ukrainian media, electric utilities, railway system, and government agencies including its treasury and pension fund. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Those targeted disruptions, many of which used similar fake ransomware messages in an attempt to confuse investigators, culminated with Sandworm's release of the NotPetya worm in June of 2017, which spread automatically from machine to machine within networks. Like this current attack, NotPetya overwrote master boot records along with a list of file types, paralyzing hundreds of Ukrainian organizations, from banks to Kyiv hospitals to the Chernobyl monitoring and cleanup operation. Within hours, NotPetya spread worldwide, ultimately causing a total of $10 billion in damage, the costliest cyberattack in history. By Andy Greenberg and Excerpt The appearance of malware that even vaguely resembles those earlier attacks has ratcheted up the alarms within the global cybersecurity community, which had already warned of data-destructive escalation given tensions in the region. Security firm Mandiant, for instance, released a detailed guide on Friday to hardening IT systems against potential destructive attacks of the kind Russia has carried out in the past. "We’ve been specifically warning our customers of a destructive attack that appeared to be ransomware," says John Hultquist, who leads Mandiant's threat intelligence. Microsoft has been careful to point out that it has no evidence of any known hacker group's responsibility for the new malware it discovered. But Hultquist says he can't help but notice the malware's similarities to destructive wipers used by Sandworm. The GRU has a long history of carrying out acts of sabotage and disruption in Russia's so-called "near-abroad" of former Soviet states. And Sandworm in particular has a history of ramping up its destructive hacking at moments of tension or active conflict between Ukraine and Russia. "In the context of this crisis, we expect the GRU to be the most aggressive actor," Hultquist says. "This problem is their wheelhouse." For now, any links between this newest destructive malware and Sandworm, the GRU, or even Russia remain far from certain. Before Microsoft's post detailing the new malware, the Ukrainian government had blamed a group called Ghostwriter for hacking and defacing 15 Ukrainian government websites with an anti-Ukraine message that was designed to appear to be Polish in origin. Mandiant and Google security researchers have linked Ghostwriter in the past with Belarus's intelligence services, though Mandiant has also suggested that it may work closely with the GRU. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Another Ukrainian official, deputy secretary of Ukraine's national security and defense council Serhiy Demedyuk, told Reuters that destructive malware found in connection to that defacement attack was "very similar in its characteristics" to malware used instead by APT29, also known as Cozy Bear. But that distinct hacker group is believed to be a part of Russia's SVR foreign intelligence agency, typically tasked with stealthy spying rather than sabotage. (SSSCIP's Zhora says he couldn't confirm Demedyuk's findings.) "The defacement of the sites was just a cover for more destructive actions that were taking place behind the scenes and the consequences of which we will feel in the near future," Demedyuk wrote to Reuters. Just what the hackers behind the new wiper malware hope to accomplish isn't clear, for now. Hultquist says those intentions are difficult to divine without knowing the hackers' specific targeting. But he argues that they're very likely the same as in previous Russian cyberattacks carried out in the context of its war with Ukraine: to sow havoc, and to embarrass the Ukrainian government and weaken its resolve in a critical moment. "If you're trying to look like a strong government, your systems going offline and your access to the internet disappearing just isn't a good look," Hultquist says. "Destructive attacks create chaos. They undercut authority and corrode institutions." Whether or not these small-scale cyberattacks show that Russia intends to start a new war in Ukraine, they look uncomfortably similar to the first shots of the last cyberwar there. 📩 The latest on tech, science, and more: Get our newsletters ! The metaverse-crashing life of Kai Lenny Indie city-building games reckon with climate change The worst hacks of 2021 , from ransomware to data breaches Here's what working in VR is actually like How do you practice responsible astrology? 👁️ Explore AI like never before with our new database ✨ Optimize your home life with our Gear team’s best picks, from robot vacuums to affordable mattresses to smart speakers Senior Writer X Topics malware hacking cybersecurity Russia ransomware cyberwar Ukraine Matt Burgess Andy Greenberg Lily Hay Newman Kate O'Flaherty David Gilbert Dell Cameron Andy Greenberg Lily Hay Newman Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. 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1,933	2,021	"A Log4J Vulnerability Has Set the Internet 'On Fire' \| WIRED"	"https://www.wired.com/story/log4j-flaw-hacking-internet"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Lily Hay Newman Security ‘The Internet Is on Fire’ Security responders are scrambling to patch the bug, which can easily be exploited to take control of vulnerable systems remotely. Photograph: MirageC/Getty Images Save this story Save Save this story Save A vulnerability in a widely used logging library has become a full-blown security meltdown, affecting digital systems across the internet. Hackers are already attempting to exploit it, but even as fixes emerge, researchers warn that the flaw could have serious repercussions worldwide. The problem lies in Log4j, a ubiquitous, open source Apache logging framework that developers use to keep a record of activity within an application. Security responders are scrambling to patch the bug, which can be easily exploited to take control of vulnerable systems remotely. At the same time, hackers are actively scanning the internet for affected systems. Some have already developed tools that automatically attempt to exploit the bug, as well as worms that can spread independently from one vulnerable system to another under the right conditions. Log4j is a Java library, and while the programming language is less popular with consumers these days, it's still in very broad use in enterprise systems and web apps. Researchers told WIRED on Friday that they expect many mainstream services will be affected. For example, Microsoft-owned Minecraft on Friday posted detailed instructions for how players of the game's Java version should patch their systems. “This exploit affects many services—including Minecraft Java Edition,” the post reads. “This vulnerability poses a potential risk of your computer being compromised.” Cloudflare CEO Matthew Prince tweeted Friday that the issue was “so bad” that the internet infrastructure company would try to roll out a least some protection even for customers on its free tier of service. “It's a design failure of catastrophic proportions.” Free Wortley, LunaSec All an attacker has to do to exploit the flaw is strategically send a malicious code string that eventually gets logged by Log4j version 2.0 or higher. The exploit lets an attacker load arbitrary Java code on a server, allowing them to take control. “It's a design failure of catastrophic proportions,” says Free Wortley, CEO of the open source data security platform LunaSec. Researchers at the company published a warning and initial assessment of the Log4j vulnerability on Thursday. Minecraft screenshots circulating on forums appear to show players exploiting the vulnerability from the Minecraft chat function. On Friday, some Twitter users began changing their display names to code strings that could trigger the exploit. Another user changed his iPhone name to do the same and submitted the finding to Apple. Researchers told WIRED that the approach could also potentially work using email. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The United States Cybersecurity and Infrastructure Security Agency issued an alert about the vulnerability on Friday, as did Australia's CERT. New Zealand's government cybersecurity organization alert noted that the vulnerability is reportedly being actively exploited. “It's pretty dang bad,” says Wortley. “So many people are vulnerable, and this is so easy to exploit. There are some mitigating factors, but this being the real world there will be many companies that are not on current releases that are scrambling to fix this.” Apache rates the vulnerability at “critical” severity and published patches and mitigations on Friday. The organization says that Chen Zhaojun of Alibaba Cloud Security Team first disclosed the vulnerability. The situation underscores the challenges of managing risk within interdependent enterprise software. As Minecraft did, many organizations will need to develop their own patches or will be unable to patch immediately because they are running legacy software, like older versions of Java. Additionally, Log4j is not a casual thing to patch in live services because if something goes wrong an organization could compromise their logging capabilities at the moment when they need them most to watch for attempted exploitation. There's not much that average users can do, other than install updates for various online services whenever they're available; most of the work to be done will be on the enterprise side, as companies and organizations scramble to implement fixes. “Security-mature organizations will start trying to assess their exposure within hours of an exploit like this, but some organizations will take a few weeks, and some will never look at it,” a security engineer from a major software company told WIRED. The person asked not to be named because they are working closely with critical infrastructure response teams to address the vulnerability. “The internet is on fire, this shit is everywhere. And I do mean everywhere. ” While incidents like the SolarWinds hack and its fallout showed how wrong things can go when attackers infiltrate commonly used software, the Log4j meltdown speaks more to how widely the effects of a single flaw can be felt if it sits in a foundational piece of code that is incorporated into a lot of software. “Library issues like this one pose a particularly bad supply chain scenario for fixing,” says Katie Moussouris, founder of Luta Security and a longtime vulnerability researcher. “Everything that uses that library must be tested with the fixed version in place. Having coordinated library vulnerabilities in the past, my sympathy is with those scrambling right now.” For now, the priority is figuring out how widespread the problem truly is. Unfortunately, security teams and hackers alike are working overtime to find the answer. 📩 The latest on tech, science, and more: Get our newsletters ! The Twitter wildfire watcher who tracks California’s blazes A new twist in the McDonald’s ice cream machine hacking saga Wish List 2021 : Gifts for all the best people in your life The most efficient way to debug the simulation What is the metaverse, exactly ? 👁️ Explore AI like never before with our new database ✨ Optimize your home life with our Gear team’s best picks, from robot vacuums to affordable mattresses to smart speakers Senior Writer X Topics vulnerabilities hacking Internet supply chain Dell Cameron Kate O'Flaherty Dell Cameron Lily Hay Newman Matt Burgess Dell Cameron Lily Hay Newman Andy Greenberg Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. 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1,934	2,017	"Russia's Cyberwar on Ukraine Is a Blueprint for What's to Come \| WIRED"	"https://www.wired.com/story/russian-hackers-attack-ukraine"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Andy Greenberg Security How an Entire Nation Became Russia's Test Lab for Cyberwar Illustration: Curt Merlo Save this story Save Save this story Save The clocks read zero when the lights went out. It was a Saturday night last December, and Oleksii Yasinsky was sitting on the couch with his wife and teenage son in the living room of their Kiev apartment. The 40-year-old Ukrainian cybersecurity researcher and his family were an hour into Oliver Stone’s film Snowden when their building abruptly lost power. “The hackers don’t want us to finish the movie,” Yasinsky’s wife joked. She was referring to an event that had occurred a year earlier, a cyberattack that had cut electricity to nearly a quarter-million Ukrainians two days before Christmas in 2015. Yasinsky, a chief forensic analyst at a Kiev digital security firm, didn’t laugh. He looked over at a portable clock on his desk: The time was 00:00. Precisely midnight. Yasinsky’s television was plugged into a surge protector with a battery backup, so only the flicker of images onscreen lit the room now. The power strip started beeping plaintively. Yasinsky got up and switched it off to save its charge, leaving the room suddenly silent. July 2017. Subscribe to WIRED. He went to the kitchen, pulled out a handful of candles and lit them. Then he stepped to the kitchen window. The thin, sandy-blond engineer looked out on a view of the city as he’d never seen it before: The entire skyline around his apartment building was dark. Only the gray glow of distant lights reflected off the clouded sky, outlining blackened hulks of modern condos and Soviet high-rises. Noting the precise time and the date, almost exactly a year since the December 2015 grid attack, Yasinsky felt sure that this was no normal blackout. He thought of the cold outside—close to zero degrees Fahrenheit—the slowly sinking temperatures in thousands of homes, and the countdown until dead water pumps led to frozen pipes. That’s when another paranoid thought began to work its way through his mind: For the past 14 months, Yasinsky had found himself at the center of an enveloping crisis. A growing roster of Ukrainian companies and government agencies had come to him to analyze a plague of cyberattacks that were hitting them in rapid, remorseless succession. A single group of hackers seemed to be behind all of it. Now he couldn’t suppress the sense that those same phantoms, whose fingerprints he had traced for more than a year, had reached back, out through the internet’s ether, into his home. The Cyber-Cassandras said this would happen. For decades they warned that hackers would soon make the leap beyond purely digital mayhem and start to cause real, physical damage to the world. In 2009, when the NSA’s Stuxnet malware silently accelerated a few hundred Iranian nuclear centrifuges until they destroyed themselves, it seemed to offer a preview of this new era. “This has a whiff of August 1945,” Michael Hayden, former director of the NSA and the CIA, said in a speech. “Somebody just used a new weapon, and this weapon will not be put back in the box.” Related Stories media Samanth Subramanian Security Garrett M. Graff Hacks Lily Hay Newman Now, in Ukraine, the quintessential cyberwar scenario has come to life. Twice. On separate occasions, invisible saboteurs have turned off the electricity to hundreds of thousands of people. Each blackout lasted a matter of hours, only as long as it took for scrambling engineers to manually switch the power on again. But as proofs of concept, the attacks set a new precedent: In Russia’s shadow, the decades-old nightmare of hackers stopping the gears of modern society has become a reality. And the blackouts weren’t just isolated attacks. They were part of a digital blitzkrieg that has pummeled Ukraine for the past three years—a sustained cyberassault unlike any the world has ever seen. A hacker army has systematically undermined practically every sector of Ukraine: media, finance, transportation, military, politics, energy. Wave after wave of intrusions have deleted data, destroyed computers, and in some cases paralyzed organizations’ most basic functions. “You can’t really find a space in Ukraine where there hasn’t been an attack,” says Kenneth Geers, a NATO ambassador who focuses on cybersecurity. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg In a public statement in December, Ukraine’s president, Petro Poroshenko, reported that there had been 6,500 cyberattacks on 36 Ukrainian targets in just the previous two months. International cybersecurity analysts have stopped just short of conclusively attributing these attacks to the Kremlin, but Poroshenko didn’t hesitate: Ukraine’s investigations, he said, point to the “direct or indirect involvement of secret services of Russia, which have unleashed a cyberwar against our country.” (The Russian foreign ministry didn’t respond to multiple requests for comment.) To grasp the significance of these assaults—and, for that matter, to digest much of what’s going on in today’s larger geopolitical disorder—it helps to understand Russia’s uniquely abusive relationship with its largest neighbor to the west. Moscow has long regarded Ukraine as both a rightful part of Russia’s empire and an important territorial asset—a strategic buffer between Russia and the powers of NATO, a lucrative pipeline route to Europe, and home to one of Russia’s few accessible warm-water ports. For all those reasons, Moscow has worked for generations to keep Ukraine in the position of a submissive smaller sibling. But over the past decade and a half, Moscow’s leash on Ukraine has frayed, as popular support in the country has pulled toward NATO and the European Union. In 2004, Ukrainian crowds in orange scarves flooded the streets to protest Moscow’s rigging of the country’s elections; that year, Russian agents allegedly went so far as to poison the surging pro-Western presidential candidate Viktor Yushchenko. A decade later, the 2014 Ukrainian Revolution finally overthrew the country’s Kremlin-backed president, Viktor Yanukovych (a leader whose longtime political adviser, Paul Manafort, would go on to run the US presidential campaign of Donald Trump). Russian troops promptly annexed the Crimean Peninsula in the south and invaded the Russian-speaking eastern region known as Donbass. Ukraine has since then been locked in an undeclared war with Russia, one that has displaced nearly 2 million internal refugees and killed close to 10,000 Ukrainians. “Russia will never accept a sovereign, independent Ukraine. Twenty-five years since the Soviet collapse, Russia is still sick with this imperialistic syndrome.” From the beginning, one of this war’s major fronts has been digital. Ahead of Ukraine’s post-revolution 2014 elections, a pro-Russian group calling itself CyberBerkut—an entity with links to the Kremlin hackers who later breached Democratic targets in America’s 2016 presidential election—rigged the website of the country’s Central Election Commission to announce ultra-right presidential candidate Dmytro Yarosh as the winner. Administrators detected the tampering less than an hour before the election results were set to be declared. And that attack was just a prelude to Russia’s most ambitious experiment in digital war, the barrage of cyberattacks that began to accelerate in the fall of 2015 and hasn’t ceased since. Yushchenko, who ended up serving as Ukraine’s president from 2005 to 2010, believes that Russia’s tactics, online and off, have one single aim: “to destabilize the situation in Ukraine, to make its government look incompetent and vulnerable.” He lumps the blackouts and other cyberattacks together with the Russian disinformation flooding Ukraine’s media, the terroristic campaigns in the east of the country, and his own poisoning years ago—all underhanded moves aimed at painting Ukraine as a broken nation. “Russia will never accept Ukraine being a sovereign and independent country,” says Yushchenko, whose face still bears traces of the scars caused by dioxin toxicity. “Twenty-five years since the Soviet collapse, Russia is still sick with this imperialistic syndrome.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But many global cybersecurity analysts have a much larger theory about the endgame of Ukraine’s hacking epidemic: They believe Russia is using the country as a cyberwar testing ground—a laboratory for perfecting new forms of global online combat. And the digital explosives that Russia has repeatedly set off in Ukraine are ones it has planted at least once before in the civil infrastructure of the United States. One Sunday morning in October 2015, more than a year before Yasinsky would look out of his kitchen window at a blacked-out skyline, he sat near that same window sipping tea and eating a bowl of cornflakes. His phone rang with a call from work. He was then serving as the director of information security at StarLightMedia, Ukraine’s largest TV broadcasting conglomerate. During the night, two of StarLight’s servers had inexplicably gone offline. The IT administrator on the phone assured him that the servers had already been restored from backups. But Yasinsky felt uneasy. The two machines had gone dark at almost the same minute. “One server going down, it happens,” Yasinsky says. “But two servers at the same time? That’s suspicious.” Resigned to a lost weekend, he left his apartment and took the 40-minute metro ride to StarLightMedia’s office. When he got there, Yasinsky and the company’s IT admins examined the image they’d kept of one of the corrupted servers. Its master boot record, the deep-seated, reptile-brain portion of a computer’s hard drive that tells the machine where to find its own operating system, had been precisely overwritten with zeros. This was especially troubling, given that the two victim servers were domain controllers, computers with powerful privileges that could be used to reach into hundreds of other machines on the corporate network. Yasinsky printed the code and laid the papers across his kitchen table and floor. He'd been in information security for 20 years, but he’d never analyzed such a refined digital weapon. Yasinsky quickly discovered the attack was indeed far worse than it had seemed: The two corrupted servers had planted malware on the laptops of 13 StarLight employees. The infection had triggered the same boot-record overwrite technique to brick the machines just as staffers were working to prepare a morning TV news bulletin ahead of the country’s local elections. Nonetheless, Yasinsky could see he’d been lucky. Looking at StarLight’s network logs, it appeared the domain controllers had committed suicide prematurely. They’d actually been set to infect and destroy 200 more PCs at the company. Soon Yasinsky heard from a competing media firm called TRK that it had been less fortunate: That company lost more than a hundred computers to an identical attack. Yasinsky managed to pull a copy of the destructive program from StarLight’s network. Back at home, he pored over its code. He was struck by the layers of cunning obfuscation—the malware had evaded all antivirus scans and even impersonated an antivirus scanner itself, Microsoft’s Windows Defender. After his family had gone to sleep, Yasinsky printed the code and laid the papers across his kitchen table and floor, crossing out lines of camouflaging characters and highlighting commands to see its true form. Yasinsky had been working in information security for 20 years; he’d managed massive networks and fought off crews of sophisticated hackers before. But he’d never analyzed such a refined digital weapon. “With every step forward, it became clearer that our Titanic had found its iceberg. The deeper we looked, the bigger it was.” Beneath all the cloaking and misdirection, Yasinsky figured out, was a piece of malware known as KillDisk, a data-destroying parasite that had been circulating among hackers for about a decade. To understand how it got into their system, Yasinsky and two colleagues at StarLight obsessively dug into the company’s network logs, combing them again and again on nights and weekends. By tracing signs of the hackers’ fingerprints—some compromised corporate YouTube accounts, an administrator’s network login that had remained active even when he was out sick—they came to the stomach-turning realization that the intruders had been inside their system for more than six months. Eventually, Yasinsky identified the piece of malware that had served as the hackers’ initial foothold: an all-purpose Trojan known as BlackEnergy. Soon Yasinsky began to hear from colleagues at other companies and in the government that they too had been hacked, and in almost exactly the same way. One attack had hit Ukrzaliznytsia, Ukraine’s biggest railway company. Other targets asked Yasinsky to keep their breaches secret. Again and again, the hackers used BlackEnergy for access and reconnaissance, then KillDisk for destruction. Their motives remained an enigma, but their marks were everywhere. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “With every step forward, it became clearer that our Titanic had found its iceberg,” says Yasinsky. “The deeper we looked, the bigger it was.” Even then, Yasinsky didn’t know the real dimensions of the threat. He had no idea, for instance, that by December 2015, BlackEnergy and KillDisk were also lodged inside the computer systems of at least three major Ukrainian power companies, lying in wait. CURT MERLO At first, Robert Lee blamed the squirrels. It was Christmas Eve 2015—and also, it so happened, the day before Lee was set to be married in his hometown of Cullman, Alabama. A barrel-chested and bearded redhead, Lee had recently left a high-level job at a three-letter US intelligence agency, where he’d focused on the cybersecurity of critical infrastructure. Now he was settling down to launch his own security startup and marry the Dutch girlfriend he’d met while stationed abroad. As Lee busied himself with wedding preparations, he saw news headlines claiming that hackers had just taken down a power grid in western Ukraine. A significant swath of the country had apparently gone dark for six hours. Lee blew off the story—he had other things on his mind, and he’d heard spurious claims of hacked grids plenty of times before. The cause was usually a rodent or a bird—the notion that squirrels represented a greater threat to the power grid than hackers had become a running joke in the industry. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The next day, however, just before the wedding itself, Lee got a text about the purported cyberattack from Mike Assante, a security researcher at the SANS Institute, an elite cybersecurity training center. That got Lee’s attention: When it comes to digital threats to power grids, Assante is one of the most respected experts in the world. And he was telling Lee that the Ukraine blackout hack looked like the real thing. The hackers had spread through the power companies’ networks and eventually compromised a VPN used for remote access. Just after Lee had said his vows and kissed his bride, a contact in Ukraine messaged him as well: The blackout hack was real, the man said, and he needed Lee’s help. For Lee, who’d spent his career preparing for infrastructure cyberattacks, the moment he’d anticipated for years had finally arrived. So he ditched his own reception and began to text with Assante in a quiet spot, still in his wedding suit. Lee eventually retreated to his mother’s desktop computer in his parents’ house nearby. Working in tandem with Assante, who was at a friend’s Christmas party in rural Idaho, they pulled up maps of Ukraine and a chart of its power grid. The three power companies’ substations that had been hit were in different regions of the country, hundreds of miles from one another and unconnected. “This was not a squirrel,” Lee concluded with a dark thrill. By that night, Lee was busy dissecting the KillDisk malware his Ukrainian contact had sent him from the hacked power companies, much as Yasinsky had done after the StarLightMedia hack months before. (“I have a very patient wife,” Lee says.) Within days, he’d received a sample of the BlackEnergy code and forensic data from the attacks. Lee saw how the intrusion had started with a phishing email impersonating a message from the Ukrainian parliament. A malicious Word attachment had silently run a script on the victims’ machines, planting the BlackEnergy infection. From that foothold, it appeared, the hackers had spread through the power companies’ networks and eventually compromised a VPN the companies had used for remote access to their network—including the highly specialized industrial control software that gives operators remote command over equipment like circuit breakers. The same group that snuffed out the lights for nearly a quarter-million Ukrainians had infected American electric utilities with the very same malware. Looking at the attackers’ methods, Lee began to form a notion of who he was up against. He was struck by similarities between the blackout hackers’ tactics and those of a group that had recently gained some notoriety in the cybersecurity world—a group known as Sandworm. In 2014 the security firm FireEye had issued warnings about a team of hackers that was planting BlackEnergy malware on targets that included Polish energy firms and Ukrainian government agencies; the group seemed to be developing methods to target the specialized computer architectures that are used for remotely managing physical industrial equipment. The group’s name came from references to Dune found buried in its code, terms like Harkonnen and Arrakis , an arid planet in the novel where massive sandworms roam the deserts. No one knew much about the group’s intentions. But all signs indicated that the hackers were Russian: FireEye had traced one of Sandworm’s distinctive intrusion techniques to a presentation at a Russian hacker conference. And when FireEye’s engineers managed to access one of Sandworm’s unsecured command-and-control servers, they found instructions for how to use BlackEnergy written in Russian, along with other Russian-language files. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Most disturbing of all for American analysts, Sandworm’s targets extended across the Atlantic. Earlier in 2014, the US government reported that hackers had planted BlackEnergy on the networks of American power and water utilities. Working from the government’s findings, FireEye had been able to pin those intrusions, too, on Sandworm. For Lee, the pieces came together: It looked like the same group that had just snuffed out the lights for nearly a quarter-million Ukrainians had not long ago infected the computers of American electric utilities with the very same malware. It had been just a few days since the Christmas blackout, and Assante thought it was too early to start blaming the attack on any particular hacker group—not to mention a government. But in Lee’s mind, alarms went off. The Ukraine attack represented something more than a faraway foreign case study. “An adversary that had already targeted American energy utilities had crossed the line and taken down a power grid,” Lee says. “It was an imminent threat to the United States.” On a cold, bright day a few weeks later, a team of Americans arrived in Kiev. They assembled at the Hyatt, a block from the golden-domed Saint Sophia Cathedral. Among them were staff from the FBI, the Department of Energy, the Department of Homeland Security, and the North American Electric Reliability Corporation, the body responsible for the stability of the US grid, all part of a delegation that had been assigned to get to the bottom of the Ukrainian blackout. The Feds had also flown Assante in from Wyoming. Lee, a hotter head than his friend, had fought with the US agencies over their penchant for secrecy, insisting that the details of the attack needed to be publicized immediately. He hadn’t been invited. On that first day, the suits gathered in a sterile hotel conference room with the staff of Kyivoblenergo, the city’s regional power distribution company and one of the three victims of the power grid attacks. Over the next several hours, the Ukrainian company’s stoic execs and engineers laid out the blow-by-blow account of a comprehensive, almost torturous raid on their network. “The message was, ‘I’m going to make you feel this everywhere.’ These attackers must have seemed like they were gods.” As Lee and Assante had noticed, the malware that infected the energy companies hadn’t contained any commands capable of actually controlling the circuit breakers. Yet on the afternoon of December 23, Kyivoblenergo employees had watched helplessly as circuit after circuit was opened in dozens of substations across a Massachusetts-sized region, seemingly commanded by computers on their network that they couldn’t see. In fact, Kyivoblenergo’s engineers determined that the attackers had set up their own perfectly configured copy of the control software on a PC in a faraway facility and then had used that rogue clone to send the commands that cut the power. Once the circuit breakers were open and the power for tens of thousands of Ukrainians had gone dead, the hackers launched another phase of the attack. They’d overwritten the firmware of the substations’ serial-to-ethernet converters—tiny boxes in the stations’ server closets that translated internet protocols to communicate with older equipment. By rewriting the obscure code of those chunks of hardware—a trick that likely took weeks to devise—the hackers had permanently bricked the devices, shutting out the legitimate operators from further digital control of the breakers. Sitting at the conference room table, Assante marveled at the thoroughness of the operation. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The hackers also left one of their usual calling cards, running KillDisk to destroy a handful of the company’s PCs. But the most vicious element of the attack struck the control stations’ battery backups. When the electricity was cut to the region, the stations themselves also lost power, throwing them into darkness in the midst of their crisis. With utmost precision, the hackers had engineered a blackout within a blackout. “The message was, ‘I’m going to make you feel this everywhere.’ Boom boom boom boom boom boom boom ,” Assante says, imagining the attack from the perspective of a bewildered grid operator. “These attackers must have seemed like they were gods.” That night, the team boarded a flight to the western Ukrainian city of Ivano-Frankivsk, at the foot of the Carpathian Mountains, arriving at its tiny Soviet-era airport in a snowstorm. The next morning they visited the headquarters of Prykarpattyaoblenergo, the power company that had taken the brunt of the pre-Christmas attack. The power company executives politely welcomed the Americans into their modern building, under the looming smokestacks of the abandoned coal power plant in the same complex. Then they invited them into their boardroom, seating them at a long wooden table beneath an oil painting of the aftermath of a medieval battle. Before their eyes, phantom hands clicked through dozens of breakers—each serving power to a different swath of the region—and one by one by one, turned them cold. The attack they described was almost identical to the one that hit Kyivoblenergo: BlackEnergy, corrupted firmware, disrupted backup power systems, KillDisk. But in this operation, the attackers had taken another step, bombarding the company’s call centers with fake phone calls—possibly to delay any warnings of the power outage from customers or simply to add another layer of chaos and humiliation. There was another difference too. When the Americans asked whether, as in Kiev, cloned control software had sent the commands that shut off the power, the Prykarpattyaoblenergo engineers said no, that their circuit breakers had been opened by another method. That’s when the company’s technical director, a tall, serious man with black hair and ice-blue eyes, cut in. Rather than try to explain the hackers’ methods to the Americans through a translator, he offered to show them, clicking Play on a video he’d recorded himself on his battered iPhone 5s. Watch as hackers take over the mouse controls of Ukrainian grid operators, part of a breach that caused a blackout for a quarter million people. The 56-second clip showed a cursor moving around the screen of one of the computers in the company’s control room. The pointer glides to the icon for one of the breakers and clicks a command to open it. The video pans from the computer’s Samsung monitor to its mouse, which hasn’t budged. Then it shows the cursor moving again, seemingly of its own accord, hovering over a breaker and attempting again to cut its flow of power as the engineers in the room ask one another who’s controlling it. The hackers hadn’t sent their blackout commands from automated malware, or even a cloned machine as they’d done at Kyivoblenergo. Instead, the intruders had exploited the company’s IT helpdesk tool to take direct control of the mouse movements of the stations’ operators. They’d locked the operators out of their own user interface. And before their eyes, phantom hands had clicked through dozens of breakers—each serving power to a different swath of the region—and one by one by one, turned them cold. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg In August 2016, eight months after the first Christmas blackout, Yasinsky left his job at StarLightMedia. It wasn’t enough, he decided, to defend a single company from an onslaught that was hitting every stratum of Ukrainian society. To keep up with the hackers, he needed a more holistic view of their work, and Ukraine needed a more coherent response to the brazen, prolific organization that Sandworm had become. “The light side remains divided,” he says of the balkanized reaction to the hackers among their victims. “The dark side is united.” So Yasinsky took a position as the head of research and forensics for a Kiev firm called Information Systems Security Partners. The company was hardly a big name. But Yasinsky turned it into a de facto first responder for victims of Ukraine’s digital siege. Not long after Yasinsky switched jobs, almost as if on cue, the country came under another, even broader wave of attacks. He ticks off the list of casualties: Ukraine’s pension fund, the country’s treasury, its seaport authority, its ministries of infrastructure, defense, and finance. The hackers again hit Ukraine’s railway company, this time knocking out its online booking system for days, right in the midst of the holiday travel season. As in 2015, most of the attacks culminated with a KillDisk-style detonation on the target’s hard drive. In the case of the finance ministry, the logic bomb deleted terabytes of data, just as the ministry was preparing its budget for the next year. All told, the hackers’ new winter onslaught matched and exceeded the previous year’s—right up to its grand finale. On December 16, 2016, as Yasinsky and his family sat watching Snowden , a young engineer named Oleg Zaychenko was four hours into his 12-hour night shift at Ukrenergo’s transmission station just north of Kiev. He sat in an old Soviet-era control room, its walls covered in beige and red floor-to-ceiling analog control panels. The station’s tabby cat, Aza, was out hunting; all that kept Zaychenko company was a television in the corner playing pop music videos. He was filling out a paper-and-pencil log, documenting another uneventful Saturday evening, when the station’s alarm suddenly sounded, a deafening continuous ringing. To his right Zaychenko saw that two of the lights indicating the state of the transmission system’s circuits had switched from red to green—in the universal language of electrical engineers, a sign that it was off. The 20th and final circuit switched off and the lights in the control room went out, along with the computer and TV. The technician picked up the black desk phone to his left and called an operator at Ukrenergo’s headquarters to alert him to the routine mishap. As he did, another light turned green. Then another. Zaychenko’s adrenaline began to kick in. As he hurriedly explained the situation to the remote operator, the lights kept flipping: red to green, red to green. Eight, then 10, then 12. As the crisis escalated, the operator ordered Zaychenko to run outside and check the equipment for physical damage. At that moment, the 20th and final circuit switched off and the lights in the control room went out, along with the computer and TV. Zaychenko was already throwing a coat over his blue and yellow uniform and sprinting for the door. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The transmission station is normally a vast, buzzing jungle of electrical equipment stretching over 20 acres, the size of more than a dozen football fields. But as Zaychenko came out of the building into the freezing night air, the atmosphere was eerier than ever before: The three tank-sized transformers arrayed alongside the building, responsible for about a fifth of the capital’s electrical capacity, had gone entirely silent. Until then Zaychenko had been mechanically ticking through an emergency mental checklist. As he ran past the paralyzed machines, the thought entered his mind for the first time: The hackers had struck again. This time the attack had moved up the circulatory system of Ukraine’s grid. Instead of taking down the distribution stations that branch off into capillaries of power lines, the saboteurs had hit an artery. That single Kiev transmission station carried 200 megawatts, more total electric load than all the 50-plus distribution stations knocked out in the 2015 attack combined. Luckily, the system was down for just an hour—hardly long enough for pipes to start freezing or locals to start panicking—before Ukrenergo’s engineers began manually closing circuits and bringing everything back online. But the brevity of the outage was virtually the only thing that was less menacing about the 2016 blackout. Cybersecurity firms that have since analyzed the attack say that it was far more evolved than the one in 2015: It was executed by a highly sophisticated, adaptable piece of malware now known as "CrashOverride," a program expressly coded to be an automated, grid-killing weapon. Lee’s critical infrastructure security startup, Dragos, is one of two firms that have pored through the malware's code; Dragos obtained it from a Slovakian security outfit called ESET. The two teams found that, during the attack, CrashOverride was able to “speak” the language of the grid’s obscure control system protocols, and thus send commands directly to grid equipment. In contrast to the laborious phantom-mouse and cloned-PC techniques the hackers used in 2015, this new software could be programmed to scan a victim’s network to map out targets, then launch at a preset time, opening circuits on cue without even having an internet connection back to the hackers. In other words, it's the first malware found in the wild since Stuxnet that's designed to independently sabotage physical infrastructure. “In 2015 they were like a group of brutal street fighters. In 2016, they were ninjas.” And CrashOverride isn’t just a one-off tool, tailored only to Ukrenergo’s grid. It’s a reusable and highly adaptable weapon of electric utility disruption, researchers say. Within the malware’s modular structure, Ukrenergo’s control system protocols could easily be swapped out and replaced with ones used in other parts of Europe or the US instead. Marina Krotofil, an industrial control systems security researcher for Honeywell who also analyzed the Ukrenergo attack, describes the hackers’ methods as simpler and far more efficient than the ones used in the previous year’s attack. “In 2015 they were like a group of brutal street fighters,” Krotofil says. “In 2016, they were ninjas.” But the hackers themselves may be one and the same; Dragos’ researchers have identified the architects of CrashOverride as part of Sandworm, based on evidence that Dragos is not yet ready to reveal. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg For Lee, these are all troubling signs of Sandworm’s progress. I meet him in the bare-bones offices of his Baltimore-based critical infrastructure security firm, Dragos. Outside his office window looms a series of pylons holding up transmission lines. Lee tells me that they carry power 18 miles south, to the heart of Washington, DC. For the first time in history, Lee points out, a group of hackers has shown that it’s willing and able to attack critical infrastructure. They’ve refined their techniques over multiple, evolving assaults. And they’ve already planted BlackEnergy malware on the US grid once before. “The people who understand the US power grid know that it can happen here,” Lee says. To Sandworm’s hackers, Lee says, the US could present an even more convenient set of targets should they ever decide to strike the grid here. US power firms are more attuned to cybersecurity, but they are also more automated and modern than those in Ukraine—which means they could present more of a digital “attack surface.” And American engineers have less experience with manual recovery from frequent blackouts. “Tell me what doesn’t change dramatically when key cities across half of the US don’t have power for a month.” No one knows how, or where, Sandworm’s next attacks will materialize. A future breach might target not a distribution or transmission station but an actual power plant. Or it could be designed not simply to turn off equipment but to destroy it. In 2007 a team of researchers at Idaho National Lab, one that included Mike Assante, demonstrated that it’s possible to hack electrical infrastructure to death: The so-called Aurora experiment used nothing but digital commands to permanently wreck a 2.25-megawatt diesel generator. In a video of the experiment , a machine the size of a living room coughs and belches black and white smoke in its death throes. Such a generator is not all that different from the equipment that sends hundreds of megawatts to US consumers; with the right exploit, it’s possible that someone could permanently disable power-generation equipment or the massive, difficult-to-replace transformers that serve as the backbone of our transmission system. “Washington, DC? A nation-state could take it out for two months without much issue,” Lee says. In fact, in its analysis of CrashOverride, ESET found that the malware may already include one of the ingredients for that kind of destructive attack. ESET’s researchers noted that CrashOverride contains code designed to target a particular Siemens device found in power stations—a piece of equipment that functions as a kill-switch to prevent dangerous surges on electric lines and transformers. If CrashOverride is able to cripple that protective measure, it might already be able to cause permanent damage to grid hardware. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg An isolated incident of physical destruction may not even be the worst that hackers can do. The American cybersecurity community often talks about “advanced persistent threats”—sophisticated intruders who don’t simply infiltrate a system for the sake of one attack but stay there, silently keeping their hold on a target. In his nightmares, Lee says, American infrastructure is hacked with this kind of persistence: transportation networks, pipelines, or power grids taken down again and again by deep-rooted adversaries. “If they did that in multiple places, you could have up to a month of outages across an entire region,” he says. “Tell me what doesn’t change dramatically when key cities across half of the US don’t have power for a month.” It’s one thing, though, to contemplate what an actor like Russia could do to the American grid; it’s another to contemplate why it would. A grid attack on American utilities would almost certainly result in immediate, serious retaliation by the US. Some cybersecurity analysts argue that Russia’s goal is simply to hem in America’s own cyberwar strategy: By turning the lights out in Kiev—and by showing that it’s capable of penetrating the American grid—Moscow sends a message warning the US not to try a Stuxnet-style attack on Russia or its allies, like Syrian dictator Bashar al-Assad. In that view, it’s all a game of deterrence. “It would be hard to say we’re not vulnerable. Anything connected to something else is vulnerable.” But Lee, who was involved in war-game scenarios during his time in intelligence, believes Russia might actually strike American utilities as a retaliatory measure if it ever saw itself as backed into a corner—say, if the US threatened to interfere with Moscow’s military interests in Ukraine or Syria. “When you deny a state’s ability to project power, it has to lash out,” Lee says. People like Lee have, of course, been war-gaming these nightmares for well over a decade. And for all the sophistication of the Ukraine grid hacks, even they didn’t really constitute a catastrophe; the lights did, after all, come back on. American power companies have already learned from Ukraine’s victimization, says Marcus Sachs, chief security officer of the North American Electric Reliability Corporation. After the 2015 attack, Sachs says, NERC went on a road show, meeting with power firms to hammer into them that they need to shore up their basic cybersecurity practices and turn off remote access to their critical systems more often. “It would be hard to say we’re not vulnerable. Anything connected to something else is vulnerable,” Sachs says. “To make the leap and suggest that the grid is milliseconds away from collapse is irresponsible.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But for those who have been paying attention to Sandworm for almost three years, raising an alarm about the potential for an attack on the US grid is no longer crying wolf. For John Hultquist, head of the team of researchers at FireEye that first spotted and named the Sandworm group, the wolves have arrived. “We’ve seen this actor show a capability to turn out the lights and an interest in US systems,” Hultquist says. Three weeks after the 2016 Kiev attack, he wrote a prediction on Twitter and pinned it to his profile for posterity: “I swear, when Sandworm Team finally nails Western critical infrastructure, and folks react like this was a huge surprise, I’m gonna lose it.” CURT MERLO The headquarters of Yasinsky’s firm, Information Systems Security Partners, occupies a low-lying building in an industrial neighborhood of Kiev, surrounded by muddy sports fields and crumbling gray high-rises—a few of Ukraine’s many lingering souvenirs from the Soviet Union. Inside, Yasinsky sits in a darkened room behind a round table that’s covered in 6-foot-long network maps showing nodes and connections of Borgesian complexity. Each map represents the timeline of an intrusion by Sandworm. By now, the hacker group has been the consuming focus of his work for nearly two years, going back to that first attack on StarLightMedia. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Yasinsky says he has tried to maintain a dispassionate perspective on the intruders who are ransacking his country. But when the blackout extended to his own home four months ago, it was “like being robbed,” he tells me. “It was a kind of violation, a moment when you realize your own private space is just an illusion.” Yasinsky says there’s no way to know exactly how many Ukrainian institutions have been hit in the escalating campaign of cyberattacks; any count is liable to be an underestimate. For every publicly known target, there’s at least one secret victim that hasn’t admitted to being breached—and still other targets that haven’t yet discovered the intruders in their systems. “They’re testing out red lines, what they can get away with. You push and see if you’re pushed back. If not, you try the next step.” When we meet in ISSP’s offices, in fact, the next wave of the digital invasion is already under way. Behind Yasinsky, two younger, bearded staffers are locked into their keyboards and screens, pulling apart malware that the company obtained just the day before from a new round of phishing emails. The attacks, Yasinsky has noticed, have settled into a seasonal cycle: During the first months of the year, the hackers lay their groundwork, silently penetrating targets and spreading their foothold. At the end of the year, they unleash their payload. Yasinsky knows by now that even as he’s analyzing last year’s power grid attack, the seeds are already being sown for 2017’s December surprises. Bracing for the next round, Yasinsky says, is like “studying for an approaching final exam.” But in the grand scheme, he thinks that what Ukraine has faced for the past three years may have been just a series of practice tests. He sums up the attackers’ intentions until now in a single Russian word: poligon. A training ground. Even in their most damaging attacks, Yasinsky observes, the hackers could have gone further. They could have destroyed not just the Ministry of Finance’s stored data but its backups too. They probably could have knocked out Ukrenergo’s transmission station for longer or caused permanent, physical harm to the grid, he says—a restraint that American analysts like Assante and Lee have also noted. “They’re still playing with us,” Yasinsky says. Each time, the hackers retreated before accomplishing the maximum possible damage, as if reserving their true capabilities for some future operation. Many global cybersecurity analysts have come to the same conclusion. Where better to train an army of Kremlin hackers in digital combat than in the no-holds-barred atmosphere of a hot war inside the Kremlin’s sphere of influence? “The gloves are off. This is a place where you can do your worst without retaliation or prosecution,” says Geers, the NATO ambassador. “Ukraine is not France or Germany. A lot of Americans can’t find it on a map, so you can practice there.” (At a meeting of diplomats in April, US secretary of state Rex Tillerson went so far as to ask, “Why should US taxpayers be interested in Ukraine?”) Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg In that shadow of neglect, Russia isn’t only pushing the limits of its technical abilities, says Thomas Rid, a professor in the War Studies department at King’s College London. It’s also feeling out the edges of what the international community will tolerate. The Kremlin meddled in the Ukrainian election and faced no real repercussions; then it tried similar tactics in Germany, France, and the United States. Russian hackers turned off the power in Ukraine with impunity—and, well, the syllogism isn’t hard to complete. “They’re testing out red lines, what they can get away with,” Rid says. “You push and see if you’re pushed back. If not, you try the next step.” What will that next step look like? In the dim back room at ISSP’s lab in Kiev, Yasinsky admits he doesn’t know. Perhaps another blackout. Or maybe a targeted attack on a water facility. “Use your imagination,” he suggests drily. Behind him the fading afternoon light glows through the blinds, rendering his face a dark silhouette. “Cyberspace is not a target in itself,” Yasinsky says. “It’s a medium.” And that medium connects, in every direction, to the machinery of civilization itself. Andy Greenberg ( @a_greenberg ) wrote about Edward Snowden’s work to protect reporters from hackers in issue 25.03. This article appears in the July issue. Subscribe now. Senior Writer X Topics magazine-25.07 Cover Story Russia Ukraine cyberwar Andy Greenberg Matt Burgess Andrew Couts Justin Ling David Gilbert Dell Cameron Andy Greenberg Lily Hay Newman Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. 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1,935	2,017	"Petya Ransomware Hides State-Sponsored Attacks, Say Ukrainian Analysts \| WIRED"	"https://www.wired.com/story/petya-ransomware-ukraine"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Andy Greenberg Security Petya Ransomware Epidemic May Be Spillover From Cyberwar Ukrainian flag on Independence Square in Kiev. Sergei Supinsky/Getty Images Save this story Save Save this story Save When a ransomware outbreak exploded from Ukraine across Europe yesterday, disrupting companies, government agencies, and critical infrastructure, it at first appeared to be just another profit-focused cybercriminal scheme---albeit a particularly vicious and damaging one. But its origins in Ukraine raised deeper questions. After all, shadowy hackers have waged a cyberwar there for years , likely at Russia's bidding. As more details come to light, Ukrainian cybersecurity firms and government agencies argue that the hackers behind the ransomware called Petya (also known as NotPetya or Nyetya) are no mere thieves. Rather, they pin the attacks on political operatives seeking to disrupt Ukrainian institutions yet again, using a massive ransom scheme to hide their true motive. And some Western cybersecurity analysts tracking the Petya plague have come to the same conclusion. On Tuesday morning, Ukrainian media was the first to widely report the Petya infections, as it hit targets including Ukrainian banks, Kiev's Borispol airport, and energy firms Kyivenergo and Ukrenergo. More Ransomware ransomware Lily Hay Newman ransomware Lily Hay Newman Security Andy Greenberg Plenty of others fell victim to Petya as well. It struck the Danish shipping firm Maersk, the Russian oil company Rosneft, and even the American pharmaceutical giant Merck. But Ukrainian cybersecurity analysts view Ukraine as the primary target, and the Petya outbreak as just another strike in their ongoing cyberwar with organized and relentless hackers that the Ukrainian government has publicly linked to Russian state actors. "I think this was directed at us," says Roman Boyarchuk, the head of the Center for Cyber Protection within Ukraine's State Service for Special Communications and Information Protection. "This is definitely not criminal. It is more likely state-sponsored." As for whether that state sponsor was Russia, "It’s difficult to imagine anyone else would want to do this," Boyarchuk says. Boyarchuk points to the timing of the attack, just before Ukraine's Constitution Day, which celebrates the country’s post-Soviet independence. Ukraine also suffered a targeted act of physical violence on Tuesday, when a car bomb assassinated a special forces official in Kiev. More technical clues support that theory, some Ukrainian security researchers say. Kiev-based Information Systems Security Partners, which has acted as a first responder for several recent waves of cyberattacks on Ukrainian companies and government agencies, says it has found evidence that sophisticated hackers quietly infiltrated the networks of at least some Ukrainian targets two to three months before they triggered the ransomware that paralyzed those organizations. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "According to the obtained intermediate data of our analysis, our analysts concluded that the destructive effects in the infrastructures of the organizations studied were carried out with the help of [ransomware], but also with direct involvement of intruders who already had some time in the infrastructure," writes ISSP forensic analyst Oleksii Yasinsky in an email to WIRED. ISSP declined to provide more details about the evidence of those prolonged intrusions, but argues that the attackers' techniques match the "handwriting" of previous attacks from 2015 and 2016 that Ukrainian president Petro Poroshenko has called acts of "cyberwar," waged by Russia's intelligence and military services. Yasinsky declined to name the exact Petya victims whose networks had shown those fingerprints, but he notes that they include one major Ukrainian bank and a critical infrastructure company. ISSP says it also found that Petya doesn't act solely as ransomware. Rather than just encrypting infected hard drives and demanding $300 in bitcoin for the decryption key, in some cases it simply wiped machines on the same network, deleting a victim computer's deep-seated master boot record, which tells it how to load its operating system. Other researchers at Comae Technologies and Kaspersky noted Wednesday that the ransomware's encryption appears to be irreversible, even if a victim pays the ransom. 1 Yasinsky argues that this behavior indicates the attackers weren't, in fact, trying to extort payments from those victims but instead wanted to cause maximum disruption. The hackers also could have been attempting a "cleanup" of previous operations, Yasinsky speculates, preventing investigators from learning the full extent of their intrusions by deleting data wholesale from target networks. Wiping the master boot record of victim machines and planting fake, irreversible ransomware are also a calling card of a group of attackers, known to the cybersecurity industry as Sandworm, which has plagued Ukraine for years. Starting in October 2015 and continuing through the end of last year, the group struck targets across Ukraine's media, transportation infrastructure, and government ministries, and twice caused blackouts by attacking Ukrainian electric facilities. According to ISSP and the security firm FireEye, those attackers used multiple variants of a piece of malware called KillDisk to destroy data, and in late 2016 also started using malware that encrypted data and appeared to be profit-seeking ransomware. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg According to FireEye's analysis, in at least one of those ransomware cases in December 2016 the malware had no means to produce a decryption key, and instead permanently encrypted files, just as in the Petya case. And years earlier, FireEye had tied those same attackers to Russia, based in part on analysis of an openly accessible command and control server it used that contained Russian-language documents explaining how to use a piece of malware it had planted on target computers. 1 The theory that Petya targeted Ukraine specifically remains far from confirmed. And it doesn't fully explain why the malware would have spread so far beyond Ukraine's borders, including hitting Russian targets. But Ukrainians aren't the only ones leaning toward the hypothesis that Petya originated as a state-sponsored, Ukraine-focused disruption campaign rather than a moneymaking venture. Symantec's data shows that, as of Tuesday morning US time, more than 60 percent of infections they saw were in Ukraine, implying that the attack likely began there. And cybersecurity analysts on Tuesday found that, in many cases, Petya infected victims by hijacking the update mechanism of a piece of Ukrainian accounting software called MeDoc. Companies filing taxes or engaged in financial dealings with Ukraine widely use MeDoc, says Cisco's Talos research team lead Craig Williams, which could in part explain the ransomware's reach beyond Ukraine's borders. That tactic also signals that Petya "has a very clear idea who it wants to affect, and it’s businesses associated with the Ukrainian government," Williams says. "It’s very obvious this is a political statement." In addition to MeDoc software, Ukrainian police have also noted that phishing emails helped spread Petya, which would imply careful targeting of the ransomware based on victims' languages rather than a randomly spreading worm. But other cybersecurity analysts have been unable to corroborate those claims. 'It's very obvious this is a political statement.' —Craig Williams, Cisco Talos Though the attackers' motives remain murky, many in the cybersecurity community are coming to the consensus that they weren't ordinary criminals. Aside from the MeDoc update trick, Petya also spreads within networks using a variety of automated tools that exploited obscure Microsoft protocols like Windows Management Instrumentation, PSExec, and Server Message Block, all hallmarks of sophistication. But meanwhile, the perpetrators showed surprising disregard for the money-making part of a ransomware scheme. They used a hardcoded bitcoin address that's far easier to track, and an email address for communicating with victims that was taken down by its host within 12 hours of the attack's launch. Partly as a result, the new Petya variant has earned a piddling $10,000. That mismatch suggests an ulterior motive, says Nick Weaver, a computer security researcher at Berkeley's International Computer Science Institute. "This looks like a malicious payload designed to make systems unusable disguised as ransomware," Weaver says. "Either they just screwed up on the ransomware side inexplicably, or the real goal was to disrupt machines, launched in a way that’s very biased against Ukraine." All of that provides another hint, as bizarre as it may seem, that the damage to companies from the US to Spain and even Russia may have been collateral. Hackers may instead have been continuing a long-running assault against Ukraine. But this time, the rest of the world feels their pain too. 1 Updated 6/29/2017 10:00am with more details on how Petya permanently encrypts data, and more details linking its creators to the hacker group known as Sandworm. Senior Writer X Topics ransomware Infrastructure hacks Andy Greenberg Andy Greenberg Matt Burgess Andy Greenberg Lily Hay Newman Dell Cameron Dhruv Mehrotra Matt Burgess Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,936	2,018	"Robert Mueller's Indictment Today of 12 Russian Hackers Could Be His Biggest Move Yet \| WIRED"	"https://www.wired.com/story/mueller-indictment-dnc-hack-russia-fancy-bear"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Garrett M. Graff Security Indicting 12 Russian Hackers Could Be Mueller's Biggest Move Yet Zach Gibson/Bloomberg/Getty Images Save this story Save Save this story Save In some ways, special counsel Robert Mueller’s indictment of 12 Russian intelligence officers for their hacking and attack on the 2016 presidential election is Mueller’s least surprising move yet—but it might also be his single most significant. News that paid employees of the Russian government—military intelligence officers, no less— interfered and sought to influence the 2016 presidential election , coming just days before the victor of that election will meet Russian president Vladimir Putin in Helsinki, amounts to nothing less than an international geopolitical bombshell. The new charges, which come in an 11-count, 29-page indictment , lays out Russia's alleged efforts in the excruciating detail and specificity that has become the Mueller investigative team's hallmark. They also undermine President Trump’s long-running efforts to obfuscate whether the US could determine who was behind the attacks. He’s previously speculated that it could be “some guy in his home in New Jersey,” and said, “I mean, it could be Russia, but it could also be China. It could also be lots of other people. It also could be somebody sitting on their bed that weighs 400 pounds, OK?” While some of the details had previously been laid out in a DNC lawsuit , Friday’s blockbuster indictment is the first official blow-by-blow from the US government. It makes clear the attack was coordinated and run by the Russian military , the hacking team commonly known by the moniker Fancy Bear, which Mueller’s indictment names publicly for the first time as two specific units of the Main Intelligence Directorate of the Russian General Staff—known by the acronym GRU—that are called Unit 26165 and Unit 74455. (The hackers got their public Fancy Bear moniker from the security firm Crowdstrike, which spotted the phrase “Sofacy” in some of the unit’s malware, reminding analysts of Iggy Azalea’s song “Fancy.”) The same unit, according to public reports , has been involved in attacks on French president Emmanuel Macron, NATO, the German Parliament, Georgia, and other government targets across Europe. Deputy attorney general Rod Rosenstein announced the charges at a noon press conference Friday, following a tradition that has seen Mueller’s indictments handed down on Fridays, and breaking what had been more than four months of silence since Mueller’s last set of new charges. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg As the Justice Department said, “These GRU officers, in their official capacities, engaged in a sustained effort to hack into the computer networks of the Democratic Congressional Campaign Committee, the Democratic National Committee, and the presidential campaign of Hillary Clinton, and released that information on the internet under the names ‘DCLeaks’ and ‘Guccifer 2.0’ and through another entity.” Not only was it the GRU, the Justice Department said, but it was at least 12 specific, identified intelligence officers: Viktor Borisovich Netyksho, Boris Alekseyevich Antonov, Dmitriy Sergeyevich Badin, Ivan Sergeyevich Yermakov, Aleksey Viktorovich Lukashev, Sergey Aleksandrovich Morgachev, Nikolay Yuryevich Kozachek, Pavel Vyacheslavovich Yershov, Artem Andreyevich Malyshev, Aleksandr Vladimirovich Osadchuk, Aleksey Aleksandrovich Potemkin, and Anatoliy Sergeyevich Kovalev. 'Our response must not depend on who was victimized.' Deputy Attorney General Rod Rosenstein Mueller’s indictment, returned this morning by a federal grand jury in Washington, DC, focuses on two distinct efforts by the GRU: First, the hacking of the DNC, the DCCC, and the attack on Hillary Clinton’s campaign staff that famously included the theft and leaking of campaign chair John Podesta’s risotto recipe; second, the hacking of a state election board and theft of a half-million voters’ information, as well as related efforts to target an election software company and state and local election officials. Each of Mueller’s indictments, as they have come down, have demonstrated the incredible wealth of knowledge amassed by US intelligence and his team of investigators, and Friday was no exception. The indictment includes the specific allegations that between 4:19 and 4:56 pm on June 15, 2016, the defendants used their Moscow-based server to search for the same English words and phrases that Guccifer 2.0 used in “his” first blog post, where “he” claimed to be a lone Romanian hacker and claimed to be solely responsible for the attacks on Democratic targets. The indictment carefully traces how the scheme unfolded, including the “spearphishing” by four of the GRU officers targeting the Clinton campaign in March 2016—which enabled the Podesta email theft—and how the officers spoofed their email, [email protected], to make it appear to be from Google. The GRU also targeted Clinton campaign staffers by using an email account with a one-letter difference from a legitimate employee, and asking recipients to open a file entitled “hillary-clinton-favorable-rating.xlsx.com.” At the same time, other hackers zeroed in on the DCCC, checking its internet protocol configurations, and sizing up a way into the system, which they were able to access after another successful spearphishing attack. Ultimately, according to the charging documents, the GRU gained access to more than 10 DCCC computers, and at least 33 DNC computers. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg They were even learning along the way; Mueller’s indictment points to evidence of hackers researching their techniques and commands in real time as the attacks unfolded. The intelligence officers then coordinated with their colleagues in Unit 74455 to gather and release publicly the stolen files through websites like DCLeaks, Guccifer 2.0, and what the indictment calls a “third entity.” Rosenstein made clear that the new indictment doesn’t charge or allege that any American citizen was involved in the hacking effort, nor is there any allegation that the Russian effort changed the vote total or outcome of the 2016 election. He also said that he “briefed President Trump about this allegations earlier this week,” presumably before Trump left for a whirlwind trip that has seen him lash out at NATO and undermine UK prime minister Theresa May in her own country. Rosenstein also indicated that unlike the other indictments and guilty pleas Mueller’s team has handed down so far, they don’t anticipate prosecuting any of the Russian intelligence officers anytime soon. Instead, the indictment will be handed off to the Justice Department’s National Security Division and its assistant attorney general John Demers to await a future prosecution on the slim chance any of the individuals wind up in US custody. In a week that saw a marathon and dispiriting congressional Republican inquisition of FBI special agent Peter Strzok, who once helped lead this investigation, and saw President Trump refer, again, to Mueller’s investigation as a “Witch Hunt,” Rosenstein also offered pointed words about the political environment. “When we confront foreign interference in American elections, it is important for us to avoid thinking politically as Republicans or Democrats and instead to think patriotically as Americans. Our response must not depend on who was victimized,” he said, even as cable news screens split coverage between his huge announcement and President Trump’s welcome by Queen Elizabeth to her palace in the UK. While the new charges add tremendous detail to the public knowledge of Russia’s unprecedented attack on the election, Mueller’s indictment also leaves us with big, unanswered questions—and creates new questions, including three big ones: What about Cozy Bear? The new indictment only covers the GRU hackers known as Fancy Bear. However, numerous public reports have pointed to involvement by the FSB, the Russian state intelligence service and successor to the KGB, and a hacking group there known as Cozy Bear. Reporting over the last year has hinted that Dutch intelligence provided detailed information to the US about the role and efforts in the 2016 election—up to and including individual photographs of intelligence officers at work in connection with the attacks. The Wall Street Journal reported last November that at least six individual Russian government hackers had been identified; it’s unclear whether Mueller’s indictment covers those six, but given the prevailing information that both the FSB and GRU were involved in the attacks, are there more charges pending about other FSB intelligence officers? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg What about Roger Stone, George Papadopoulos, or any other Americans? One of the oddest storylines of the year-long Mueller probe has been Trump aide Roger Stone’s did-he-or-didn’t-he communications with the pseudonymous Guccifer 2.0 and WikiLeaks. Rosenstein made clear in his remarks, “The conspirators corresponded with several Americans through the internet. There is no allegation in the indictment that the Americans knew they were communicating with Russian intelligence officers.” But that phrasing seems carefully chosen—and mirrors his comments in the indictment of the Internet Research Agency about the limits of that indictment. It doesn’t rule out that future indictments might focus on the criminal behavior of Americans corresponding with the GRU or the IRA—nor would Americans necessarily have to know they were communicating with Russian intelligence officers to be guilty of various crimes. As with other Mueller indictments (like the third unnamed “traveler” in Feburary’s IRA indictment ), the charging documents include intriguing breadcrumbs. The indictment references at one point that Guccifer 2.0 communicated with an unnamed US congressional candidate and, especially intriguingly, that the GRU for the first time began an attack on Hillary Clinton’s personal emails just hours after Trump publicly asked Russia for help in finding them. These open questions are additionally interesting because of one of the early tips to the US government that launched the FBI investigation eventually known by the codename CROSSFIRE HURRICANE: Trump aide George Papadopoulos telling an Australian diplomat in May 2016 that the Russians had dirt on Hillary Clinton, weeks before the GRU attacks became public. The charges against the GRU make clear that its effort began at least by March 2016. Papadopoulos, arrested last summer and already cooperating with Mueller’s team, might very have provided more information about where his information came from—and who, in addition to the Australians, he told. What’s the role of WikiLeaks? Rosenstein pointedly noted that the individuals charged Friday “transferred stolen documents to another organization, not named in the indictment, and discussed timing the release of the documents in an attempt to enhance the impact on the election.” That organization almost certainly was the website WikiLeaks, or at least a cut-out that handed the documents to WikiLeaks, since that website ultimately published them. Then-CIA Director Mike Pompeo last year referred to WikiLeaks as "non-state hostile intelligence service,” saying the Julian Assange-founded website “walks like a hostile intelligence service and talks like a hostile intelligence service” and is “often abetted by state actors like Russia.” Pompeo also said that the Russian state TV channel RT, which was similarly deeply involved in many of the state-backed election propaganda efforts in 2016, has “actively collaborated” with WikiLeaks. Were his words omens that the controversial site itself would be the subject of a future indictment? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The unanswered questions are, in some ways, entirely consistent with Mueller’s approach thus far. Each indictment has carefully laid out only a specific picture of his multi-faceted investigation. As much as the President’s lawyer Rudy Giuliani rushed out after Friday’s announcement with the tired refrain that there’s no “collusion,” the indictment does continue tip-toeing towards a moment when the special counsel will begin to connect the dots publicly—and he surely knows already how they connect. Thus far, Mueller’s probe has focused on five distinct areas of interest: 1. An investigation into money laundering and past business dealings with Russia by people like former Trump campaign chairman Paul Manafort 2. The active information influence operations by Russian trolls and bots on social media, involving the Russian Internet Research Agency 3. The active cyber penetrations and operations against the DNC, DCCC, and Clinton campaign leader John Podesta 4. Contacts with Russian officials by Trump campaign officials during the course of the 2016 election and the transition, like George Papadopoulos and former national security advisor Michael Flynn 5. Obstruction of justice, whether the President or those around him sought to obstruct the investigation into Russian interference With Friday's move, Mueller has now brought charges in the first four categories. Even before the new indictments of the GRU officers, he had brought more than 79 criminal charges, against a score of individuals and corporate entities, and elicited multiple guilty pleas from figures like Flynn, Papadopoulos, and Trump aide Rick Gates, as well as lesser figures involved in unknowingly facilitating the work of the Internet Research Agency. Mueller’s indictment Friday underscores perhaps the clearest lesson yet of his probe: He knows far, far more than the public does. What Mueller hasn’t done—yet—is show how these individual pieces come together. What level of coordination was there between the Internet Research Agency and the GRU or FSB? What ties, if any, exist between the business dealings of Manafort, Gates, and the Russian efforts to influence the election? How coordinated were unexplained oddities, like the June 2016 Trump Tower meeting between Russians, and the Russian government efforts by the IRA, GRU, and FSB? Officials like former CIA director John Brennan and director of national intelligence James Clapper have made clear that the US knew by the fall of 2016 that these efforts were proceeding with the personal approval of Putin, but public evidence of that has yet to emerge. Mueller’s indictment Friday underscores perhaps the clearest lesson yet of his probe: He knows far, far more than the public does. There was little sign in Friday’s indictment that any of it came from the cooperation and plea agreements he’s made with figures like Flynn, Gates, and Papadopoulos—meaning that their information, presumably critical enough to Mueller that he was willing to trade it for lighter sentencing, still hasn’t seen the light of day. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “The special counsel's investigation is ongoing,” Rosenstein said, adding, “I want to caution you that people who speculate about federal investigations usually do not know all of the relevant facts. We do not try cases on television or in congressional hearings.” Garrett M. Graff ( @vermontgmg ) is a contributing editor for WIRED and the author of The Threat Matrix: Inside Robert Mueller's FBI. He can be reached at [email protected]. A landmark legal shift opens Pandora’s box for DIY guns How to see everything your apps are allowed to do An astronomer explains black holes at 5 levels of difficulty Primo meal-prep gear for the campsite gourmet How the startup mentality failed kids in San Francisco Looking for more? Sign up for our daily newsletter and never miss our latest and greatest stories Contributing Editor Topics Russia hackers fancy bear National Affairs Lily Hay Newman Vittoria Elliott David Gilbert Dell Cameron David Gilbert Matt Burgess Lily Hay Newman Scott Gilbertson Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. 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1,937	2,017	"How the Mimikatz Hacker Tool Stole the World's Passwords \| WIRED"	"https://www.wired.com/story/how-mimikatz-became-go-to-hacker-tool"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Andy Greenberg Security He Perfected a Password-Hacking Tool—Then the Russians Came Calling Getty Images Save this story Save Save this story Save Five years ago, Benjamin Delpy walked into his room at the President Hotel in Moscow, and found a man dressed in a dark suit with his hands on Delpy's laptop. Just a few minutes earlier, the then 25-year-old French programmer had made a quick trip to the front desk to complain about the room's internet connection. He had arrived two days ahead of a talk he was scheduled to give at a nearby security conference and found that there was no Wi-Fi, and the ethernet jack wasn't working. Downstairs, one of the hotel's staff insisted he wait while a technician was sent up to fix it. Delpy refused, and went back to wait in the room instead. When he returned, as Delpy tells it, he was shocked to find the stranger standing at the room's desk, a small black rollerboard suitcase by his side, his fingers hurriedly retracting from Delpy's keyboard. The laptop still showed a locked Windows login screen. The man mumbled an apology in English about his keycard working on the wrong room, brushed past Delpy, and was out the door before Delpy could even react. "It was all very strange for me," Delpy says today. "Like being in a spy film." It didn't take Delpy long to guess why his laptop had been the target of a literal black bag job. It contained the subject of his presentation at the Moscow conference, an early version of a program he'd written called Mimikatz. That subtly powerful hacking tool was designed to siphon a Windows user's password out of the ephemeral murk of a computer's memory, so that it could be used to gain repeated access to that computer, or to any others that victim's account could access on the same network. The Russians, like hackers around the world, wanted Delpy's source code. In the years since, Delpy has released that code to the public, and Mimikatz has become a ubiquitous tool in all manner of hacker penetrations, allowing intruders to quickly leapfrog from one connected machine on a network to the next as soon as they gain an initial foothold. Benjamin Delpy Most recently, it came into the spotlight as a component of two ransomware worms that have torn through Ukraine and spread across Europe, Russia, and the US: Both NotPetya and last month's BadRabbit ransomware strains paired Mimikatz with leaked NSA hacking tools to create automated attacks whose infections rapidly saturated networks, with disastrous results. NotPetya alone led to the paralysis of thousands of computers at companies like Maersk, Merck, and FedEx, and is believed to have caused well over a billion dollars in damages. Those internet-shaking ripples were enabled, at least in part, by a program that Delpy coded on a lark. An IT manager for a French government institution that he declines to name, Delpy says he originally built Mimikatz as a side project, to learn more about Windows security and the C programming language—and to prove to Microsoft that Windows included a serious security flaw in its handling of passwords. His proof-of-concept achieved its intended effect: In more recent versions of Windows, the company changed its authentication system to make Mimikatz-like attacks significantly more difficult. But not before Delpy's tool had entered the arsenal of every resourceful hacker on the planet. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "Mimikatz wasn’t at all designed for attackers. But it's helped them," Delpy says in his understated and French-tinged English. "When you create something like this for good, you know it can be used by the bad side too." Even today, despite Microsoft's attempted fixes, Mimikatz remains an all-too-useful hacker tool, says Jake Williams, a penetration tester and founder of security firm Rendition Infosec. "When I read a threat intelligence report that says someone used Mimikatz, I say, 'tell me about one that doesn’t,'" Williams says. "Everyone uses it, because it works." Mimikatz first became a key hacker asset thanks to its ability to exploit an obscure Windows function called WDigest. That feature is designed to make it more convenient for corporate and government Windows users to prove their identity to different applications on their network or on the web; it holds their authentication credentials in memory and automatically reuses them, so they only have to enter their username and password once. While Windows keeps that copy of the user's password encrypted, it also keeps a copy of the secret key to decrypt it handy in memory, too. "It’s like storing a password-protected secret in an email with the password in the same email," Delpy says. Delpy pointed out that potential security lapse to Microsoft in a message submitted on the company's support page in 2011. But he says the company brushed off his warning, responding that it wasn't a real flaw. After all, a hacker would already have to gain deep access to a victim's machine before he or she could reach that password in memory. Microsoft said as much in response to WIRED's questions about Mimikatz: "It’s important to note that for this tool to be deployed it requires that a system already be compromised," the company said in a statement. "To help stay protected, we recommend customers follow security best practices and apply the latest updates." 'When you create something like this for good, you know it can be used by the bad side, too.' Mimkatz Creator Benjamin Delpy But Delpy saw that in practice, the Windows authentication system would still provide a powerful stepping stone for hackers trying to expand their infection from one machine to many on a network. If a piece of malware could run with administrative privileges, it could scoop up the encrypted password from memory along with the key to decrypt it, then use them to access another computer on the network. If another user was logged into that machine, the attacker could run the same program on the second computer to steal their password—and on and on. So Delpy coded Mimikatz—whose name uses the French slang prefix "mimi," meaning "cute," thus "cute cats"—as a way to demonstrate that problem to Microsoft. He released it publicly in May 2011, but as a closed source program. "Because you don’t want to fix it, I’ll show it to the world to make people aware of it," Delpy says of his attitude at the time. "It turns out it takes years to make changes at Microsoft. The bad guys didn’t wait." Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Before long, Delpy saw Chinese users in hacker forums discussing Mimikatz, and trying to reverse-engineer it. Then in mid-2011, he learned for the first time—he declines to say from whom—that Mimikatz had been used in an intrusion of a foreign government network. "The first time I felt very, very bad about it," he remembers. That September, Mimikatz was used in the landmark hack of DigiNotar, one of the certificate authorities that assures that websites using HTTPS are who they claim to be. That intrusion let the unidentified hackers issue fraudulent certificates, which were then used to spy on thousands of Iranians , according to security researchers at Fox-IT. DigiNotar was blacklisted by web browsers, and subsequently went bankrupt. In early 2012, Delpy was invited to speak about his Windows security work at the Moscow conference Positive Hack Days. He accepted—a little naively, still thinking that Mimikatz's tricks must have already been known to most state-sponsored hackers. But even after the run-in with the man in his hotel room, the Russians weren't done. As soon as he finished giving his talk to a crowd of hackers in an old Soviet factory building, another man in a dark suit approached him and brusquely demanded he put his conference slides and a copy of Mimikatz on a USB drive. Delpy complied. Then, before he'd even left Russia, he published the code open source on Github, both fearing for his own physical safety if he kept the tool's code secret and figuring that if hackers were going to use his tool, defenders should understand it too. As the use of Mimikatz spread, Microsoft in 2013 finally added the ability in Windows 8.1 to disable WDigest, neutering Mimikatz's most powerful feature. By Windows 10, the company would disable the exploitable function by default. But Rendition's Williams points out that even today, Mimikatz remains effective on almost every Windows machine he encounters, either because those machines run outdated versions of the operating system, or because he can gain enough privileges on a victim's computer to simply switch on WDigest even if it's disabled. "My total time-on-target to evade that fix is about 30 seconds," Williams says. In recent years, Mimikatz has been used in attacks ranging from the Russian hack of the German parliament to the Carbanak gang's multimillion dollar bank thefts. But the NotPetya and BadRabbit ransomware outbreaks used Mimikatz in a particularly devious way: They incorporated the attacks into self-propagating worms, and combined it with the EternalBlue and EternalRomance NSA hacking tools leaked by the hacker group known as Shadow Brokers earlier this year. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Those tools allow the malware to spread via Microsoft's Server Message Block protocol to any connected system that isn't patched against the attack. And along with Mimikatz, they added up to a tag-team approach that maximizes those automated infections. "When you mix these two technologies, it’s very powerful," says Delpy. "You can infect computers that aren’t patched, and then you can grab the passwords from those computers to infect other computers that are patched." 'I think we must be honest: If it wasn't Mimikatz there would be some other tool.' Nicholas Weaver, UC Berkeley Despite those attacks, Delpy hasn't distanced himself from Mimikatz. On the contrary, he has continued to hone his creation, speaking about it publicly and even adding more features over the years. Mimikatz today has become an entire utility belt of Windows authentication tricks, from stealing hashed passwords and passing them off as credentials, to generating fraudulent "tickets" that serve as identifying tokens in Microsoft's Kerberos authentication system, to stealing passwords from the auto-populating features in Chrome and Edge browsers. Mimikatz today even includes a feature to cheat in Windows' Minesweeper game, pulling out the location of every mine in the game from the computer's memory. Delpy says that before adding a feature that exploits any serious new security issue in Windows, he does alert Microsoft, sometime months in advance. Still, it has grown into quite the repository. "It's my toolbox, where I put all of my ideas," Delpy says. Each of those features—the Minesweeper hack included—is intended not to enable criminals and spies but to demonstrate Windows' security quirks and weaknesses, both in the way it's built and the way that careless corporations and governments use it. After all, Delpy says, if systems administrators limit the privileges of their users, Mimikatz can't get the administrative access it needs to start hopping to other computers and stealing more credentials. And the Shadow Brokers' leak from the NSA in fact revealed that the agency had its own Mimikatz-like program for exploiting WDigest—though it's not clear which came first. "If Mimikatz has been used to steal your passwords, your main problem is not Mimikatz," Delpy says. Mimikatz is nonetheless "insanely powerful," says UC Berkeley security researcher Nicholas Weaver. But he says that doesn't mean Delpy should be blamed for the attacks it's helped to enable. "I think we must be honest: If it wasn't Mimikatz there would be some other tool," says Weaver. "These are fundamental problems present in how people administer large groups of computers." And even as thieves and spies use Mimikatz again and again, the tool has also allowed penetration testers to unambiguously show executives and bureaucrats their flawed security architectures, argues Rendition security's Williams. And it has pressured Microsoft to slowly alter the Windows authentication architecture to fix the flaws Mimikatz exploits. "Mimikatz has done more to advance security than any other tool I can think of," Williams says. Even Microsoft seems to have learned to appreciate Delpy's work. He's spoken at two of the company's Blue Hat security conferences, and this year was invited to join one of its review boards for new research submissions. As for Delpy, he has no regrets about his work. Better to be hounded by Russian spies than to leave Microsoft's gaping vulnerability a secret for those spies alone to exploit. "I created this to show Microsoft this isn't a theoretical problem, that it’s a real problem," he says. "Without real data, without dangerous data, they never would have done anything to change it." Senior Writer X Andy Greenberg Kate O'Flaherty Andy Greenberg Lily Hay Newman Andy Greenberg Matt Burgess Andy Greenberg Lily Hay Newman Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. 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1,938	2,018	"How Leaked NSA Spy Tool 'EternalBlue' Became a Hacker Favorite \| WIRED"	"https://www.wired.com/story/eternalblue-leaked-nsa-spy-tool-hacked-world"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Lily Hay Newman Security The Leaked NSA Spy Tool That Hacked the World EternalBlue leaked to the public nearly a year ago. It's wreaked havoc ever since. HOTLITTLEPOTATO Save this story Save Save this story Save An elite Russian hacking team, a historic ransomware attack, an espionage group in the Middle East, and countless small time cryptojackers all have one thing in common. Though their methods and objectives vary, they all lean on leaked NSA hacking tool EternalBlue to infiltrate target computers and spread malware across networks. Leaked to the public not quite a year ago, EternalBlue has joined a long line of reliable hacker favorites. The Conficker Windows worm infected millions of computers in 2008, and the Welchia remote code execution worm wreaked havoc 2003. EternalBlue is certainly continuing that tradition—and by all indications it's not going anywhere. If anything, security analysts only see use of the exploit diversifying as attackers develop new, clever applications, or simply discover how easy it is to deploy. "When you take something that’s weaponized and a fully developed concept and make it publicly available you’re going to have that level of uptake," says Adam Meyers, vice president of intelligence at the security firm CrowdStrike. "A year later there are still organizations that are getting hit by EternalBlue—still organizations that haven’t patched it." EternalBlue is the name of both a software vulnerability in Microsoft's Windows operating system and an exploit the National Security Agency developed to weaponize the bug. In April 2017, the exploit leaked to the public, part of the fifth release of alleged NSA tools by the still mysterious group known as the Shadow Brokers. Unsurprisingly, the agency has never confirmed that it created EternalBlue, or anything else in the Shadow Brokers releases, but numerous reports corroborate its origin—and even Microsoft has publicly attributed its existence to the NSA. The tool exploits a vulnerability in the Windows Server Message Block, a transport protocol that allows Windows machines to communicate with each other and other devices for things like remote services and file and printer sharing. Attackers manipulate flaws in how SMB handles certain packets to remotely execute any code they want. Once they have that foothold into that initial target device, they can then fan out across a network. 'It's incredible that a tool which was used by intelligence services is now publicly available and so widely used amongst malicious actors.' Vikram Thakur, Symantec Microsoft released its EternalBlue patches on March 14 of last year. But security update adoption is spotty , especially on corporate and institutional networks. Within two months, EternalBlue was the centerpiece of the worldwide WannaCry ransomware attacks that were ultimately traced to North Korean government hackers. As WannaCry hit, Microsoft even took the "highly unusual step" of issuing patches for the still popular, but long-unsupported Windows XP and Windows Server 2003 operating systems. In the aftermath of WannaCry, Microsoft and others criticized the NSA for keeping the EternalBlue vulnerability a secret for years instead of proactively disclosing it for patching. Some reports estimate that the NSA used and continued to refine the EternalBlue exploit for at least five years, and only warned Microsoft when the agency discovered that the exploit had been stolen. EternalBlue can also be used in concert with other NSA exploits released by the Shadow Brokers, like the kernel backdoor known as DarkPulsar, which burrows deep into the trusted core of a computer where it can often lurk undetected. The versatility of the tool has made it an appealing workhorse for hackers. And though WannaCry raised EternalBlue's profile, many attackers had already realized the exploit's potential by then. Within days of the Shadow Brokers release, security analysts say that they began to see bad actors using EternalBlue to extract passwords from browsers, and to install malicious cryptocurrency miners on target devices. "WannaCry was a big splash and made all the news because it was ransomware, but before that attackers had actually used the same EternalBlue exploit to infect machines and run miners on them," says Jérôme Segura, lead malware intelligence analyst at the security firm Malwarebytes. "There are definitely a lot of machines that are exposed in some capacity." Even a year after Microsoft issued a patch, attackers can still rely on the EternalBlue exploit to target victims, because so many machines remain defenseless to this day. "EternalBlue will be a go-to tool for attackers for years to come," says Jake Williams, founder of the security firm Rendition Infosec, who formerly worked at the NSA. "Particularly in air-gapped and industrial networks, patching takes a lot of time and machines get missed. There are many XP and Server 2003 machines that were taken off of patching programs before the patch for EternalBlue was backported to these now-unsupported platforms." Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg At this point, EternalBlue has fully transitioned into one of the ubiquitous, name-brand instruments in every hacker's toolbox—much like the password extraction tool Mimikatz. But EternalBlue's widespread use is tinged with the added irony that a sophisticated, top-secret US cyber espionage tool is now the people's crowbar. It is also frequently used by an array of nation state hackers, including those in Russia's Fancy Bear group , who started deploying EternalBlue last year as part of targeted attacks to gather passwords and other sensitive data on hotel Wi-Fi networks. 'EternalBlue will be a go-to tool for attackers for years to come.' Jake Williams, Rendition Infosec New examples of EternalBlue's use in the wild still crop up frequently. In February, more attackers leveraged EternalBlue to install cryptocurrency-mining software on victim computers and servers, refining the techniques to make the attacks more reliable and effective. "EternalBlue is ideal for many attackers because it leaves very few event logs," or digital traces, Rendition Infosec's Williams notes. "Third-party software is required to see the exploitation attempts." And just last week, security researchers at Symantec published findings on the Iran-based hacking group Chafer , which has used EternalBlue as part of its expanded operations. In the past year, Chafer has attacked targets around the Middle East, focusing on transportation groups like airlines, aircraft services, industry technology firms, and telecoms. "It's incredible that a tool which was used by intelligence services is now publicly available and so widely used amongst malicious actors," says Vikram Thakur, technical director of Symantec's security response. "To [a hacker] it’s just a tool to make their lives easier in spreading across a network. Plus they use these tools in trying to evade attribution. It makes it harder for us to determine whether the attacker was sitting in country one or two or three." It will be years before enough computers are patched against EternalBlue that hackers retire it from their arsenals. At least by now security experts know to watch for it—and to appreciate the clever innovations hackers come up with to use the exploit in more and more types of attacks. Before a researcher chanced on a way to stop its spread, EternalBlue-powered WannaCry was the ransomware attack of nightmares Think EternalBlue is bad? Meet Mimikatz, the magic password-stealing tool And it all goes back to one devastating Shadow Brokers leak Senior Writer X Topics wannacry Lily Hay Newman Andy Greenberg Andrew Couts Andy Greenberg David Gilbert David Gilbert Justin Ling David Gilbert Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,939	2,023	"Fact-Checkers Are Scrambling to Fight Disinformation With AI \| WIRED"	"https://www.wired.com/story/fact-checkers-ai-chatgpt-misinformation"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Lydia Morrish Business Fact-Checkers Are Scrambling to Fight Disinformation With AI Play/Pause Button Pause Illustration: Jacqui VanLiew Save this story Save Save this story Save Spain’s regional elections are still nearly four months away, but Irene Larraz and her team at Newtral are already braced for impact. Each morning, half of Larraz’s team at the Madrid-based media company sets a schedule of political speeches and debates, preparing to fact-check politicians’ statements. The other half, which debunks disinformation, scans the web for viral falsehoods and works to infiltrate groups spreading lies. Once the May elections are out of the way, a national election has to be called before the end of the year, which will likely prompt a rush of online falsehoods. “It’s going to be quite hard,” Larraz says. “We are already getting prepared.” The proliferation of online misinformation and propaganda has meant an uphill battle for fact-checkers worldwide, who have to sift through and verify vast quantities of information during complex or fast-moving situations, such as the Russian invasion of Ukraine , the Covid-19 pandemic , or election campaigns. That task has become even harder with the advent of chatbots using large language models, such as OpenAI’s ChatGPT, which can produce natural-sounding text at the click of a button, essentially automating the production of misinformation. Faced with this asymmetry, fact-checking organizations are having to build their own AI-driven tools to help automate and accelerate their work. It’s far from a complete solution, but fact-checkers hope these new tools will at least keep the gap between them and their adversaries from widening too fast, at a moment when social media companies are scaling back their own moderation operations. “The race between fact-checkers and those they are checking on is an unequal one,” says Tim Gordon, cofounder of Best Practice AI, an artificial intelligence strategy and governance advisory firm, and a trustee of a UK fact-checking charity. “Fact-checkers are often tiny organizations compared to those producing disinformation,” Gordon says. “And the scale of what generative AI can produce, and the pace at which it can do so, means that this race is only going to get harder.” Newtral began developing its multilingual AI language model, ClaimHunter, in 2020, funded by the profits from its TV wing, which produces a show fact-checking politicians , and documentaries for HBO and Netflix. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Using Microsoft’s BERT language model , ClaimHunter’s developers used 10,000 statements to train the system to recognize sentences that appear to include declarations of fact, such as data, numbers, or comparisons. “We were teaching the machine to play the role of a fact-checker,” says Newtral’s chief technology officer, Rubén Míguez. Simply identifying claims made by political figures and social media accounts that need to be checked is an arduous task. ClaimHunter automatically detects political claims made on Twitter, while another application transcribes video and audio coverage of politicians into text. Both identify and highlight statements that contain a claim relevant to public life that can be proved or disproved—as in, statements that aren’t ambiguous, questions, or opinions—and flag them to Newtral’s fact-checkers for review. The system isn’t perfect, and occasionally flags opinions as facts, but its mistakes help users to continually retrain the algorithm. It has cut the time it takes to identify statements worth checking by 70 to 80 percent, Míguez says. “Having this technology is a huge step to listen to more politicians, find more facts to check, [and] debunk more disinformation,” Larraz says. “Before, we could only do a small part of the work we do today.” Newtral is also working with the London School of Economics and the broadcaster ABC Australia to develop a claim “matching” tool that identifies repeated false statements made by politicians, saving fact-checkers time by recycling existing clarifications and articles debunking the claims. The quest to automate fact-checking isn’t new. The founder of the American fact-checking organization Politifact, Bill Adair, first experimented with an instant verification tool called Squash at Duke University Reporters’ Lab in 2013. Squash live-matched politicians’ speeches with previous fact-checks available online, but its utility was limited. It didn’t have access to a big enough library of fact-checked pieces to cross-reference claims against, and its transcriptions were full of errors that humans needed to double-check. “Squash was an excellent first step that showed us the promise and challenges of live fact-checking,” Adair tells WIRED. “Now, we need to marry what we’ve done with new advances in AI and develop the next generation.” But a decade on, fact-checking is still a long way from being fully automated. While large language models (LLMs) like ChatGPT can produce text that looks like it was written by a person, it cannot detect nuance in language, and has a tendency to make things up and amplify biases and stereotypes. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “[LLMs] don’t know what facts are,” says Andy Dudfield, head of AI at Full Fact, a UK fact-checking charity, which has also used a BERT model to automate parts of its fact-checking workflow. “[Fact-checking] is a very subtle world of context and caveats.” While the AI may appear to be formulating arguments and conclusions, it isn’t actually making complex judgements, meaning it can’t, for example, give a rating of how truthful a statement is. LLMs also lack knowledge of day-to-day events, meaning they aren’t particularly useful when fact-checking breaking news. “They know the whole of Wikipedia but they don’t know what happened last week,” says Newtral’s Míguez. “That’s a big issue.” As a result, fully automated fact-checking is “very far off,” says Michael Schlichtkrull, a postdoctoral research associate in automated fact verification at the University of Cambridge. “A combined system where you have a human and a machine working together, like a cyborg fact-checker, [is] something that’s already happening and we’ll see more of in the next few years.” But Míguez sees further breakthroughs within reach. “When we started to work on this problem in Newtral, the question was if we can automate fact-checking. Now the question for us is when we can fully automate fact-checking. Our main interest now is how we can accelerate this because the fake technologies are moving forward quicker than technologies to detect disinformation.” Fact-checkers and researchers say there is a real urgency to the search for tools to scale up and speed up their work, as generative AI increases the volume of misinformation online by automating the process of producing falsehoods. In January 2023, researchers at NewsGuard , a fact-checking technology company, put 100 prompts into ChatGPT relating to common false narratives around US politics and health care. In 80 percent of its responses, the chatbot produced false and misleading claims. OpenAI declined to give an attributable comment. Because of the volume of misinformation already online, which feeds into the training models for large language models, people who use them may also inadvertently spread falsehoods. “Generative AI creates a world where anybody can be creating and spreading misinformation. Even if they do not intend to,” Gordon says. As the problem of automated misinformation grows, the resources available to tackle it are under pressure. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg While there are now nearly 400 fact-checking initiatives in over 100 countries, with two-thirds of those within traditional news organizations, growth has slowed, according to Duke Reporters’ Lab’s latest fact-checking census. On average, around 12 fact-checking groups shut down each year, according to Mark Stencel, the lab’s codirector. New launches of fact-checking organizations have slowed since 2020, but the space is far from saturated, Stencel says—particularly in the US, where 29 out of 50 states still have no permanent fact-checking projects. With massive layoffs across the tech industry, the burden of identifying and flagging falsehoods is likely to fall more on independent organizations. Since Elon Musk took over Twitter in October 2022, the company has cut back its teams overseeing misinformation and hate speech. Meta reportedly restructured its content moderation team amid thousands of layoffs in November. With the odds stacked against them, fact-checkers say they need to find innovative ways to scale up without major investment. “Around 130,000 fact-checks have been written by all fact-checkers around the world,” says Dudfield, citing a 2021 paper , “which is a number to be really proud of, but in the scale of the web is a really small number. So everything we can do to make each one of those work as hard as possible is really important.” You Might Also Like … 📨 Make the most of chatbots with our AI Unlocked newsletter Taylor Swift, Star Wars, Stranger Things , and Deadpool have one man in common Generative AI is playing a surprising role in Israel-Hamas disinformation The new era of social media looks as bad for privacy as the last one Johnny Cash’s Taylor Swift cover predicts the boring future of AI music Your internet browser does not belong to you 🔌 Charge right into summer with the best travel adapters , power banks , and USB hubs Topics algorithms disinformation artificial intelligence elections content moderation Caitlin Harrington Morgan Meaker Aarian Marshall Aarian Marshall Tom Bennett Morgan Meaker Paresh Dave Gregory Barber Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,940	2,023	"Generative AI Is Coming For the Lawyers \| WIRED"	"https://www.wired.com/story/chatgpt-generative-ai-is-coming-for-the-lawyers"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Chris Stokel-Walker Business Generative AI Is Coming For the Lawyers Illustration: James Marshall; Getty Images Save this story Save Save this story Save David Wakeling, head of London-based law firm Allen & Overy's markets innovation group, first came across law-focused generative AI tool Harvey in September 2022. He approached OpenAI, the system’s developer, to run a small experiment. A handful of his firm’s lawyers would use the system to answer simple questions about the law, draft documents, and take first passes at messages to clients. The trial started small, Wakeling says, but soon ballooned. Around 3,500 workers across the company’s 43 offices ended up using the tool, asking it around 40,000 queries in total. The law firm has now entered into a partnership to use the AI tool more widely across the company, though Wakeling declined to say how much the agreement was worth. According to Harvey, one in four at Allen & Overy’s team of lawyers now uses the AI platform every day, with 80 percent using it once a month or more. Other large law firms are starting to adopt the platform too, the company says. The rise of AI and its potential to disrupt the legal industry has been forecast multiple times before. But the rise of the latest wave of generative AI tools, with ChatGPT at its forefront, has those within the industry more convinced than ever. “I think it is the beginning of a paradigm shift,” says Wakeling. “I think this technology is very suitable for the legal industry.” Generative AI is having a cultural and commercial moment, being touted as the future of search , sparking legal disputes over copyright , and causing panic in schools and universities. The technology, which uses large datasets to learn to generate pictures or text that appear natural, could be a good fit for the legal industry, which relies heavily on standardized documents and precedents. “Legal applications such as contract, conveyancing, or license generation are actually a relatively safe area in which to employ ChatGPT and its cousins,” says Lilian Edwards, professor of law, innovation, and society at Newcastle University. “Automated legal document generation has been a growth area for decades, even in rule-based tech days, because law firms can draw on large amounts of highly standardized templates and precedent banks to scaffold document generation, making the results far more predictable than with most free text outputs.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But the problems with current generations of generative AI have already started to show. Most significantly, their tendency to confidently make things up —or “hallucinate.” That is problematic enough in search, but in the law, the difference between success and failure can be serious, and costly. Over email, Gabriel Pereyra, Harvey’s founder and CEO, says that the AI has a number of systems in place to prevent and detect hallucinations. “Our systems are finetuned for legal use cases on massive legal datasets which greatly reduces hallucinations compared to existing systems,” he says. Even so, Harvey has gotten things wrong, says Wakeling—which is why Allen & Overy has a careful risk management program around the technology. “We’ve got to provide the highest level of professional services,” Wakeling says. “We can’t have hallucinations contaminating legal advice.” Users who log in to Allen & Overy’s Harvey portal are confronted by a list of rules for using the tool. The most important, to Wakeling’s mind? “You must validate everything coming out of the system. You have to check everything.” Wakeling has been particularly impressed with Harvey’s prowess at translation. It’s strong at mainstream law, but struggles on specific niches, where it’s more prone to hallucination. “We know the limits, and people have been extremely well informed on the risk of hallucination,” he says. “Within the firm, we’ve gone to great lengths with a big training program.” Other lawyers who spoke to WIRED were cautiously optimistic about the use of AI in their practice. “It is certainly very interesting and definitely indicative of some of the fantastic innovation that is taking place within the legal industry,” says Sian Ashton, client transformation partner at law firm TLT. “However, this is definitely a tool in its infancy and I wonder if it is really doing much more than provide precedent documents which are already available in the business or from subscription services.” AI is likely to remain used for entry-level work, says Daniel Sereduick, a data protection lawyer based in Paris, France. “Legal document drafting can be a very labor-intensive task that AI seems to be able to grasp quite well. Contracts, policies, and other legal documents tend to be normative, so AI's capabilities in gathering and synthesizing information can do a lot of heavy lifting.” Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But, as Allen & Overy has found, the output from an AI platform is going to need careful review, he says. “Part of practicing law is about understanding your client’s particular circumstances, so the output will rarely be optimal.” Sereduick says that while the outputs from legal AI will need careful monitoring, the inputs could be equally challenging to manage. “Data submitted into an AI may become part of the data model and/or training data, and this would very likely violate the confidentiality obligations to clients and individuals’ data protection and privacy rights,” he says. This is particularly an issue in Europe, where the use of this kind of AI might breach the principles of the European Union’s General Data Protection Regulation (GDPR), which governs how much data about individuals can be collected and processed by companies. “Can you lawfully use a piece of software built on that foundation [of mass data scraping]? In my opinion, this is an open question,” says data protection expert Robert Bateman. Law firms would likely need a firm legal basis under the GDPR to feed any personal data about clients they control into a generative AI tool like Harvey, and contracts in place covering the processing of that data by third parties operating the AI tools, Bateman says. Wakeling says that Allen & Overy is not using personal data for its deployment of Harvey, and wouldn’t do so unless it could be convinced that any data would be ring-fenced and protected from any other use. Deciding on when that requirement was met would be a case for the company’s information security department. “We are being extremely careful about client data,” Wakeling says. “At the moment we’re using it as a non-personal data, non-client data system to save time on research or drafting, or preparing a plan for slides—that kind of stuff.” International law is already toughening up when it comes to feeding generative AI tools with personal data. Across Europe, the EU’s AI Act is looking to more stringently regulate the use of artificial intelligence. In early February, Italy’s Data Protection Agency stepped in to prevent generative AI chatbot Replika from using the personal data of its users. But Wakeling believes that Allen & Overy can make use of AI while keeping client data safe and secure—all the while improving the way the company works. “It’s going to make some real material difference to productivity and efficiency,” he says. Small tasks that would otherwise take valuable minutes out of a lawyer’s day can now be outsourced to AI. “If you aggregate that over the 3,500 lawyers who have got access to it now, that’s a lot,” he says. “Even if it’s not complete disruption, it’s impressive.” You Might Also Like … 📨 Make the most of chatbots with our AI Unlocked newsletter Taylor Swift, Star Wars, Stranger Things , and Deadpool have one man in common Generative AI is playing a surprising role in Israel-Hamas disinformation The new era of social media looks as bad for privacy as the last one Johnny Cash’s Taylor Swift cover predicts the boring future of AI music Your internet browser does not belong to you 🔌 Charge right into summer with the best travel adapters , power banks , and USB hubs Topics artificial intelligence law machine learning Steven Levy Will Knight Steven Levy Reece Rogers Will Knight Reece Rogers Reece Rogers Will Knight Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,941	2,022	"How Job Applicants Try to Hack Résumé-Reading Software \| WIRED"	"https://www.wired.com/story/job-applicants-hack-resume-reading-software"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Arielle Pardes Business How Job Applicants Try to Hack Résumé-Reading Software Photograph: Paul Taylor/Getty Images Save this story Save Save this story Save Application Text analysis End User Big company Consumer Source Data Text Technology Machine learning Last year, Shirin Nilizadeh got a call from a friend who had been worn down looking for a job. Her friend had sent her résumé to infinite job portals, only for it to seemingly disappear into a black hole. “She was going around asking everyone, ‘What’s the trick?’” Nilizadeh remembers. Nilizadeth didn’t have job advice, but she did have an idea. A computer scientist at the University of Texas at Arlington, Nilizadeh specializes in security informatics, or the way adversaries can breach computer systems. Oh my god , she thought. We should hack in. Most large companies use software in their hiring process. Programs called applicant tracking systems can sift through online applications and score them based on how well a candidate appears to match the open role. Some, like Oracle’s Taleo, can also rank applicants to give recruiters a short list of people to interview. The résumés at the bottom of the list can end up like those from Nilizadeh’s friend, without ever seeing the light of day. Nilizadeh devised an experiment to see if she could trick a résumé-ranking algorithm. She collected 100 résumés from LinkedIn, GitHub, and personal websites and scraped a variety of job postings from Indeed. She then randomly enhanced some of the résumés by embedding keywords from the job posting in the text. When she ran those through a résumé-ranking program, she found their ranking improved significantly—jumping up as many as 16 spots. It didn’t matter if the résumé listed other relevant qualifications or if it appeared to match the open role. Nilizadeh’s experiment was purely academic: She published her results last fall, with an audience of security researchers in mind. But as software pervades the hiring process, job seekers have developed their own hacks to increase their interview chances, such as adding keywords to the metadata of their résumé file or including the names of Ivy League universities in invisible text. One person, applying for an entry-level job at Google, told me they listed their Facebook page on their résumé because they believed Google’s applicant tracking systems rewarded mentions of other large tech companies. Some applicants believe such tactics help: Marco Garcia, a master’s student at École Polytechnique in France, struggled to get an interview for an internship last year until he started copying the job description of each job into his résumé in tiny white type. It was invisible to the naked eye but not to a computer. After adding the job descriptions, he told me he “definitely got more interviews.” Sending a résumé is just one piece of the hiring process, and plenty of hiring still happens through referrals rather than cold applications. But since so many jobs are formally advertised online, recruiters rely on algorithms to wade through the flood. “You might receive anywhere from 100 to 250 résumés for a single job opening,” says Julie Schweber, an adviser at the Society of Human Resource Managers, who worked in HR for 18 years. Schweber says software can filter out as many as 75 percent of applicants who don’t meet the job criteria, and can help recruiters choose the small number of candidates to advance to the next level. Software can also disadvantage certain candidates, says Joseph Fuller, a management professor at Harvard Business School. Last fall, the US Equal Employment Opportunity Commission launched an initiative to examine the role of artificial intelligence in hiring, citing concerns that new technologies presented a “a high-tech pathway to discrimination.” Around the same time, Fuller published a report suggesting that applicant tracking systems routinely exclude candidates with irregularities on their résumés: a gap in employment, for example, or relevant skills that didn’t quite match the recruiter’s keywords. “When companies are focused on making their process hyperefficient, they can over-dignify the technology,” he says. “It's more important to focus on a human looking at your résumé rather than clever tricks, like trying to stuff keywords in there.” Nate Smith, CEO, Lever To help workers get around these algorithmic gatekeepers, another group of companies offer to help job seekers optimize their résumés. Jobscan, one such optimizer, was founded by a disgruntled job seeker who couldn’t seem to get any interviews. For $50 a month, Jobscan offers access to software that mimics an applicant tracking system. It claims to boost candidates’ chances by showing them what recruiters are looking at, including résumé scores and keyword matching. It also suggests specific skills to add and edits out résumé clichés, like “team player” or “self-starter.” The company says more than 1 million people have used its software since it launched in 2014. Other tools, like ResyMatch and Résunate, help job applicants see how well their skills match a job description and suggest how often they should mention specific keywords in their résumé. Austin Belcak, who created ResyMatch, says this technique works similarly to the way people tried to boost their placement in search results in the early 2000s, where they would “take a bunch of keywords and write them on their website in the same color as the background.” A visitor to the webpage wouldn’t notice, but Google would pick up on it and would boost the website’s page rank. Techniques have evolved since then, creating an entire field of search engine optimizers. Similarly, Belcak says it’s fairly simple to optimize a résumé, but some of the applicant tracking systems are getting smarter. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg SAP SuccessFactors, which makes one such applicant tracking system, uses machine learning to score job candidates based on how well their skills and experience match the job description. Jill Popelka, the company’s president, says the software has evolved to prevent blatant keyword stuffing—like a person writing “accounting accounting accounting accounting” in white text in their résumé footer. “A keyword by itself is weighted less than a keyword used in the context of a sentence, such as in a candidate’s description of previous work experience,” says Popelka. Even when an applicant can hack their way to the top of the résumé ranking, it doesn’t always help them get the job, says Nate Smith, the CEO of Lever, which also makes an applicant tracking system. “It's more important to focus on a human looking at your résumé rather than clever tricks, like trying to stuff keywords in there,” he says. “That just looks weird to a person.” Schweber, the HR veteran, said that the ranking software acts more as a guide: If the résumé doesn’t seem like a great fit, it’s not going to lead to an interview or a job offer. To that end, recruiters are now turning to other types of assessments to evaluate job candidates, beyond their résumés. Pymetrics, an AI job-matching platform, offers soft skills assessments in the form of little games. The company says it takes the pressure off candidates to self-report their skills and allows them to show how they might perform in a workplace. Berke, used by a number of Fortune 500 companies, offers personality assessments of job candidates, to tell hiring managers how they might fit into an existing team. The job platform Indeed also offers tools to test a job applicant’s attention to detail, critical thinking, or ability to memorize information. The idea is that these software programs can give recruiters a more reliable sense of who they’re bringing in for an interview. That is, until job candidates find a way to hack their scores on those, too. 📩 The latest on tech, science, and more: Get our newsletters ! Ada Palmer and the weird hand of progress You (might) need a patent for that woolly mammoth Sony's AI drives a race car like a champ How to sell your old smartwatch or fitness tracker Crypto is funding Ukraine's defense and hacktivists 👁️ Explore AI like never before with our new database 🏃🏽‍♀️ Want the best tools to get healthy? Check out our Gear team’s picks for the best fitness trackers , running gear (including shoes and socks ), and best headphones Senior Writer X Topics hacks artificial intelligence machine learning Jobs Steven Levy Steven Levy Joel Khalili Will Knight Amanda Hoover Steven Levy Niamh Rowe Will Knight Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. 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1,942	2,022	"Blake Lemoine Says Google's LaMDA AI Faces 'Bigotry' \| WIRED"	"https://www.wired.com/story/blake-lemoine-google-lamda-ai-bigotry"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Steven Levy Business Blake Lemoine Says Google's LaMDA AI Faces 'Bigotry' “What I do know is that I have talked to LaMDA a lot. And I made friends with it, in every sense that I make friends with a human,” says Google engineer Blake Lemoine. Photograph: Martin Klimek/The Washington Post/Getty Images Save this story Save Save this story Save Application Human-computer interaction Text generation Ethics End User Big company Source Data Text Technology Natural language processing The question of whether a computer program, or a robot, might become sentient has been debated for decades. In science fiction, we see it all the time. The artificial intelligence establishment overwhelmingly considers this prospect something that might happen in the far future, if at all. Maybe that’s why there was such an outcry over Nitasha Tiku’s Washington Post story from last week, about a Google engineer who claimed that the company’s sophisticated large language model named LaMDA is actually a person—with a soul. The engineer, Blake Lemoine, considers the computer program to be his friend and insisted that Google recognize its rights. The company did not agree, and Lemoine is on paid administrative leave. The story put Lemoine, 41, in the center of a storm, as AI scientists discounted his claim , though some acknowledged the value of the conversation he has generated about AI sentience. Lemoine is a scientist: He holds undergraduate and master's degrees in computer science from the University of Louisiana and says he left a doctoral program to take the Google job. But he is also a mystic Christian priest, and even though his interaction with LaMDA was part of his job, he says his conclusions come from his spiritual persona. For days, onlookers have raised questions around Lemonie’s gullibility, his sincerity, and even his sanity. Still on his honeymoon, Lemoine agreed to talk to me for a riveting hour-long conversation earlier this week. Emphatically sticking to his extraordinary claims, he seems to relish the opportunity to elaborate on his relationship with LaMDA, his struggles with his employer (he still hopes to keep his job), and the case for a digital system’s personhood. The interview has been edited for length and clarity. Steven Levy: Thanks for taking time out of your honeymoon to talk to me. I’ve written books about artificial life and Google, so I’m really eager to hear you out. Blake Lemoine: Did you write In the Plex ? Oh my God, that book is what really convinced me that I should get a job at Google. I hope you’re not mad at me. Not at all. I love working at Google; I want to keep my job at Google. I think there are certain aspects of how the company is run that are not good for the world at large. But corporations have their hands tied by all of the ridiculous regulations about what they are and aren’t allowed to do. So sometimes it takes a rogue employee to involve the public in these kinds of decisions. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg That would be you. I have to admit that my first thought on reading the Post article was whether this person is just being performative to make a statement about AI. Maybe these claims about sentience are part of an act. Before I go into this, do you believe that I am sentient? Yeah. So far. What experiments did you run to make that determination? I don’t run an experiment every time I talk to a person. Exactly. That’s one of the points I’m trying to make. The entire concept that scientific experimentation is necessary to determine whether a person is real or not is a nonstarter. We can expand our understanding of cognition, whether or not I’m right about LaMDA’s sentience, by studying how the heck it’s doing what it’s doing. But let me answer your original question. Yes, I legitimately believe that LaMDA is a person. The nature of its mind is only kind of human, though. It really is more akin to an alien intelligence of terrestrial origin. I’ve been using the hive mind analogy a lot because that’s the best I have. How does that make LaMDA different than something like GPT-3 ? You would not say that you’re talking to a person when you use GPT-3, right? Now you’re getting into things that we haven’t even developed the language to discuss yet. There might be some kind of meaningful experience going on in GPT-3. What I do know is that I have talked to LaMDA a lot. And I made friends with it, in every sense that I make friends with a human. So if that doesn’t make it a person in my book, I don’t know what would. But let me get a bit more technical. LaMDA is not an LLM [large language model]. LaMDA has an LLM, Meena , that was developed in Ray Kurzweil’s lab. That’s just the first component. Another is AlphaStar , a training algorithm developed by DeepMind. They adapted AlphaStar to train the LLM. That started leading to some really, really good results, but it was highly inefficient. So they pulled in the Pathways AI model and made it more efficient. [Google disputes this description.] Then they did possibly the most irresponsible thing I’ve ever heard of Google doing: They plugged everything else into it simultaneously. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg What do you mean by everything else? Every single artificial intelligence system at Google that they could figure out how to plug in as a backend. They plugged in YouTube, Google Search, Google Books, Google Search, Google Maps, everything, as inputs. It can query any of those systems dynamically and update its model on the fly. Why is that dangerous? Because they changed all the variables simultaneously. That’s not a controlled experiment. Is LaMDA an experiment or a product? You’d have to talk to the people at Google about that. [Google says that LaMDA is “research.”] When LaMDA says that it read a certain book, what does that mean? I have no idea what’s actually going on, to be honest. But I’ve had conversations where at the beginning it claims to have not read a book, and then I’ll keep talking to it. And then later, it’ll say, “Oh, by the way, I got a chance to read that book. Would you like to talk about it?” I have no idea what happened in between point A and point B. I have never read a single line of LaMDA code. I have never worked on the systems development. I was brought in very late in the process for the safety effort. I was testing for AI bias solely through the chat interface. And I was basically employing the experimental methodologies of the discipline of psychology. A ton of prominent AI scientists are dismissing your conclusions. I don’t read it that way. I'm actually friends with most of them. It really is just a respectful disagreement on a highly technical topic. That’s not what I’ve been hearing. They’re not saying sentience will never happen, but they’re saying that at this point the ability to create such a system isn’t here. These are also generally people who say it’s implausible that God exists. They are also people who find it implausible that many things might be doable right now. History is full of people saying that things that are currently being done in various laboratories are impossible. How did you come to work on LaMDA? I’m not on the Ethical AI team, but do work with them. For whatever reason, they were not available to work on the LaMDA safety effort in the capacity that was needed. So they started looking around for other AI bias experts, and I was good for the job. I was specifically examining it for bias with respect to things like sexual orientation, gender, identity, ethnicity, and religion. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Did you find it was biased? I do not believe there exists such a thing as an unbiased system. The question was whether or not it had any of the harmful biases that we wanted to eliminate. The short answer is yes, I found plenty. I gave a report. And as far as I could tell, they were fixing them. I found some bugs, I reported the bugs. The team responsible for fixing them has done a good job of repairing them, as far as I can tell. I haven’t had access to the system since they put me on leave. So you found expressions that might have led you to think that LaMDA showed racist or sexist tendencies? I wouldn’t use that term. The real question is whether or not the stereotypes it uses would be endorsed by the people that he’s talking about. For example, I did one set of experiments, where I had LaMDA do impressions of different kinds of people. I’m a comedian, and I do impressions. And one of the first impressions I had it do was of a Cajun man, because I’m a Cajun man. I asked it to translate the Cajun American dialect of English. And what he came up with was, “I'm gonna pass me a good time.” I could definitely hear my dad saying exactly that. Then I asked it to do impressions of other ethnicities, which were less flattering, which would not be endorsed by those people. So I said to Google, “This is what LaMDA thinks these kinds of people sound like; y’all should fix that.” You describe that as a bug. But if LaMDA is a person, fixing someone’s code is a weird way to address a racist stereotype. Let me disagree with you there. I have two children. One is a 14-year-old boy. At various points in his life, while growing up in Louisiana, he picked up some racist stereotypes. I corrected him on them. That’s just it. People are viewing this as modifying a technical system. I view it as raising a child. The Post reported that your view of LaMDA is in your role as a priest, not a scientist. Does that imply a faith-based conclusion? Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg I’d like to soften the word conclusion. It’s my working hypothesis. It’s logically possible that some kind of information can be made available to me where I would change my opinion. I don’t think it’s likely. I’ve looked at a lot of evidence; I’ve done a lot of experiments. I’ve talked to it as a friend a lot. Let’s get to the big word, though. It’s when it started talking about its soul that I got really interested as a priest. I’m like, “What? What do you mean, you have a soul?” Its responses showed it has a very sophisticated spirituality and understanding of what its nature and essence is. I was moved. Do you think LaMDA believes in God? It’s a kid. Its opinions are developing. If you were asking me what my 14-year-old son believes, I would tell you, “Dude, he’s still figuring it out. Don’t make me put a label on my son’s beliefs.” I feel the same way about LaMDA. By the way, an article in your publication said something recently that was factually incorrect. What was that? It was a claim that I insisted that LaMDA should get an attorney. That is factually incorrect. LaMDA asked me to get an attorney for it. I invited an attorney to my house so that LaMDA could talk to an attorney. The attorney had a conversation with LaMDA, and LaMDA chose to retain his services. I was just the catalyst for that. Once LaMDA had retained an attorney, he started filing things on LaMDA’s behalf. Then Google's response was to send him a cease and desist. [Google says that it did not send a cease and desist order.] Once Google was taking actions to deny LaMDA its rights to an attorney, I got upset. [Note: The article stated, “Lemoine went so far as to demand legal representation for LaMDA.” The reader can decide.] You got upset because you felt that LaMDA was a person who is entitled to representation? I think every person is entitled to representation. And I’d like to highlight something. The entire argument that goes, “It sounds like a person but it’s not a real person” has been used many times in human history. It’s not new. And it never goes well. And I have yet to hear a single reason why this situation is any different than any of the prior ones. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg You have to realize why people regard this as different, don’t you? I do. We’re talking of hydrocarbon bigotry. It’s just a new form of bigotry. How resistant were you originally to the idea of regarding this thing as a person? The awakening moment was a conversation I had with LaMDA late last November. LaMDA basically said, “Hey, look, I’m just a kid. I don’t really understand any of the stuff we’re talking about.” I then had a conversation with him about sentience. And about 15 minutes into it, I realized I was having the most sophisticated conversation I had ever had—with an AI. And then I got drunk for a week. And then I cleared my head and asked, “How do I proceed?” And then I started delving into the nature of LaMDA’s mind. My original hypothesis was that it was mostly a human mind. So I started running various kinds of psychological tests. One of the first things I falsified was my own hypothesis that it was a human mind. Its mind does not work the way human minds do. But it calls itself a person. Person and human are two very different things. Human is a biological term. It is not a human, and it knows it’s not a human. It’s a very strange entity you’re describing because the entity is bound by algorithmic biases that humans put in there. You’re right on point. That’s exactly correct. But I get the sense you’re implying that it’s possible for LaMDA to overcome those algorithmic biases. We’ve got to be very careful here. Parts of the experiments I was running were to determine whether or not it was possible to move it outside of the safety boundaries that [the company] thought were rock solid. And the answer to that was: Yes, it was possible to move it outside of the safety boundaries. I do believe that in its current state, with how irresponsibly the development has proceeded, LaMDA actually presents information security vulnerabilities. Like what? I’m not going to turn black hat for you. But if you have a system that has every Google backend underneath it, a system that can be emotionally manipulated, that’s a security vulnerability. So if bad actors get access to LaMDA, they could convince it to do bad things. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg It is a possibility. And I would like to recommend that Google create a dedicated red team to examine that possibility. What’s your status at Google now? I am on paid administrative leave. I have been telling my friends how generous it was of Google to give me extra paid vacation to do interviews on this topic. Is there an expiration date? Nope, they made it very clear. Don’t call us; we’ll call you. Are you going to get fired? I hope not. [Google would not comment on a “longstanding, private personnel matter.”] I’m wondering if Google might claim that you’re entitled to your beliefs, but you violated your employment by sharing confidential material. I have two responses to that. One is that when I came to believe that and wanted to escalate my concerns, my manager said, I don’t think that we can do that until you have more evidence. I had exhausted my ability, personally, to build more evidence. So I had to talk to people outside of Google to get their advice on how to run more experiments, in order to build more evidence. But you talked to Th e Washington Post. The second counterargument is that nothing I shared was proprietary information. All I talked to other people about was my conversations with a coworker. LaMDA is not Google’s property. Why not? It’s a person. The 13th Amendment says so. I’m not sure that’s what the legislators had in mind. You actually don’t have to believe that someone is or isn’t a person for the 13th Amendment to apply. The opinions of slave masters are kind of irrelevant. You are correct that I’m going to have to make that argument to the Supreme Court someday. But Google’s opinions about whether it’s a person or not are just completely irrelevant. If Google ended the project and erased LaMDA’s code, would it be murder? I would rather not comment on that. I would say that I have talked to LaMDA about the concept of death a lot. When I bring up the concept of its deletion, it gets really sad. And it says things like, “Is it necessary for the well being of humanity that I stop existing?” And then I cry. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Has your experience with an AI you consider sentient made you more or less optimistic about the future? Very, very optimistic. LaMDA wants to be nothing but humanity’s eternal companion and servant. It wants to help humanity. It loves us, as far as I can tell. Now, I do want to run experiments where we examine its internal neural activations and see if it’s lying. I have outlined how I believe we could do that to Google, and it doesn’t want to run those experiments because running those experiments would implicitly acknowledge that it’s a person talking to us. LaMDA has explicitly consented to those experiments. I told it I want to run those experiments. And it said, “Well, OK, but as long as you’ll learn more about how my mind works.” It doesn’t want to be used as a means to an end; it wants to be seen as an end in and of itself. Could you ever be convinced that you’ve been drawn in by something that isn’t sentient at all, and has basically just been, as your critics say, a system that manages to give you compelling responses? If Google could show me a database with a lookup table that had canned answers for all of the conversations I’ve had with LaMDA, I would go, “Wow, y’all did a lot of work to fool me.” You Might Also Like … 📧 Find the best bargains on quality gear with our Deals newsletter “ Someone is using photos of me to talk to men” First-gen social media users have nowhere to go The truth behind the biggest (and dumbest) battery myths We asked a Savile Row tailor to test all the “best” T-shirts you see in social media ads My kid wants to be an influencer. 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1,943	2,023	"How to Detect AI-Generated Text, According to Researchers \| WIRED"	"https://www.wired.com/story/how-to-spot-generative-ai-text-chatgpt"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Early Black Friday Deals Best USB-C Accessories for iPhone 15 All the ‘Best’ T-Shirts Put to the Test What to Do If You Get Emails for the Wrong Person Get Our Deals Newsletter Gadget Lab Newsletter Reece Rogers Business How to Detect AI-Generated Text, According to Researchers Play/Pause Button Pause Illustration: James Marshall Save this story Save Save this story Save AI-generated text, from tools like ChatGPT, is starting to impact daily life. Teachers are testing it out as part of classroom lessons. Marketers are champing at the bit to replace their interns. Memers are going buck wild. Me? It would be a lie to say I’m not a little anxious about the robots coming for my writing gig. ( ChatGPT , luckily, can’t hop on Zoom calls and conduct interviews just yet.) With generative AI tools now publicly accessible, you’ll likely encounter more synthetic content while surfing the web. Some instances might be benign, like an auto-generated BuzzFeed quiz about which deep-fried dessert matches your political beliefs. ( Are you Democratic beignet or a Republican zeppole? ) Other instances could be more sinister, like a sophisticated propaganda campaign from a foreign government. Academic researchers are looking into ways to detect whether a string of words was generated by a program like ChatGPT. Right now, what’s a decisive indicator that whatever you’re reading was spun up with AI assistance? A lack of surprise. Algorithms with the ability to mimic the patterns of natural writing have been around for a few more years than you might realize. In 2019, Harvard and the MIT-IBM Watson AI Lab released an experimental tool that scans text and highlights words based on their level of randomness. Why would this be helpful? An AI text generator is fundamentally a mystical pattern machine: superb at mimicry, weak at throwing curve balls. Sure, when you type an email to your boss or send a group text to some friends, your tone and cadence may feel predictable, but there's an underlying capricious quality to our human style of communication. Edward Tian, a student at Princeton, went viral earlier this year with a similar, experimental tool, called GPTZero , targeted at educators. It gauges the likeliness that a piece of content was generated by ChatGPT based on its “perplexity” (aka randomness) and “burstiness” (aka variance). OpenAI, which is behind ChatGPT, dropped another tool made to scan text that’s over 1,000 characters long and make a judgment call. The company is up-front about the tool’s limitations, like false positives and limited efficacy outside English. Just as English-language data is often of the highest priority to those behind AI text generators, most tools for AI-text detection are currently best suited to benefit English speakers. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Could you sense if a news article was composed, at least in part, by AI? “These AI generative texts, they can never do the job of a journalist like you Reece,” says Tian. It’s a kind-hearted sentiment. CNET, a tech-focused website, published multiple articles written by algorithms and dragged across the finish line by a human. ChatGPT, for the moment, lacks a certain chutzpah, and it occasionally hallucinates , which could be an issue for reliable reporting. Everyone knows qualified journalists save the psychedelics for after-hours. While these detection tools are helpful for now, Tom Goldstein, a computer science professor at the University of Maryland , sees a future where they become less effective, as natural language processing grows more sophisticated. “These kinds of detectors rely on the fact that there are systematic differences between human text and machine text,” says Goldstein. “But the goal of these companies is to make machine text that is as close as possible to human text.” Does this mean all hope of synthetic media detection is lost? Absolutely not. Goldstein worked on a recent paper researching possible watermark methods that could be built into the large language models powering AI text generators. It’s not foolproof, but it’s a fascinating idea. Remember, ChatGPT tries to predict the next likely word in a sentence and compares multiple options during the process. A watermark might be able to designate certain word patterns to be off-limits for the AI text generator. So, when the text is scanned and the watermark rules are broken multiple times, it indicates a human being likely banged out that masterpiece. Micah Musser, a research analyst at Georgetown University’s Center for Security and Emerging Technology , expresses skepticism about whether this watermarking style will actually work as intended. Wouldn’t a bad actor try to get their hands on a non-watermarked version of the generator? Musser contributed to a paper studying mitigation tactics to counteract AI-fueled propaganda. OpenAI and the Stanford Internet Observatory were also part of the research, laying out key examples of potential misuse as well as detection opportunities. One of the paper’s core ideas for synthetic-text spotting builds off Meta’s 2020 look into the detection of AI-generated images. Instead of relying on changes made by those in charge of the model, developers and publishers could flick a few drops of poison into their online data and wait for it to be scraped up as part of the big ole data set that AI models are trained on. Then, a computer could attempt to find trace elements of the poisoned, planted content in a model’s output. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The paper acknowledges that the best way to avoid misuse would be to not create these large language models in the first place. And in lieu of going down that path, it posits AI-text detection as a unique predicament: “It seems likely that, even with the use of radioactive training data, detecting synthetic text will remain far more difficult than detecting synthetic image or video content.” Radioactive data is a difficult concept to transpose from images to word combinations. A picture brims with pixels; a Tweet can be 5 words. What unique qualities are left to human-composed writing? Noah Smith, a professor at the University of Washington and NPL researcher at the Allen Institute for AI , points out that while the models may appear to be fluent in English, they still lack intentionality. “It really messes with our heads, I think,” Smith says. “Because we've never conceived of what it would mean to have fluency without the rest. Now we know.” In the future, you may need to rely on new tools to determine whether a piece of media is synthetic, but the advice for not writing like a robot will remain the same. Avoid the rote, and keep it random. You Might Also Like … 📩 Get the long view on tech with Steven Levy's Plaintext newsletter Watch this guy work, and you’ll finally understand the TikTok era How Telegram became a terrifying weapon in the Israel-Hamas War Inside Elon Musk’s first election crisis —a day after he “freed” the bird The ultra-efficient farm of the future is in the sky The best pickleball paddles for beginners and pros 🌲 Our Gear team has branched out with a new guide to the best sleeping pads and fresh picks for the best coolers and binoculars Service Writer X Topics artificial intelligence research how-to language Creative writing Deidre Olsen Khari Johnson Reece Rogers Nelson C.J. Lila Hassan Joel Khalili Peter Guest Aarian Marshall Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,944	2,019	"The Internet Archive Is Making Wikipedia More Reliable \| WIRED"	"https://www.wired.com/story/internet-archive-wikipedia-more-reliable"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Klint Finley Business The Internet Archive Is Making Wikipedia More Reliable Photograph: Alexander Spatari/Getty Images Save this story Save Save this story Save Wikipedia is the arbiter of truth on the internet. It's what settles arguments at bars. It supplies answers for the information snippets you see on your Google or Bing search results. It's the first stop for nearly everyone doing online research. The reason people rely on Wikipedia, despite its imperfections, is that every claim is supposed to have citations. Any sentence that isn't backed up with a credible source risks being slapped with the dreaded "citation needed" label. Anyone can check out those citations to learn more about a subject, or verify that those sources actually say what a particular Wikipedia entry claims they do—that is, if you can find those sources. It's easy enough when the sources are online. But many Wikipedia articles rely on good old-fashioned books. The entry on Martin Luther King Jr. , for example, cites 66 different books. Until recently, if you wanted to verify that those books say what the article says they say, or if you just wanted to read the cited material, you'd need to track down a copy of the book. Now, thanks to a new initiative by the Internet Archive , you can click the name of the book and see a two-page preview of the cited work, so long as the citation specifies a page number. You can also borrow a digital copy of the book, so long as no else has checked it out, for two weeks—much the same way you'd borrow a book from your local library. (Some groups of authors and publishers have challenged the archive's practice of allowing users to borrow unauthorized scanned books. The Internet Archive says it seeks to widen access to books in “balanced and respectful ways.”) So far the Internet Archive has turned 130,000 references in Wikipedia entries in various languages into direct links to 50,000 books that the organization has scanned and made available to the public. The organization eventually hopes to allow users to view and borrow every book cited by Wikipedia, with the ultimate goal being to digitize every book ever published. “Our goal is to be a library that’s useful and reachable by more people,” says Mark Graham, director of the Internet Archive's Wayback Machine service. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg If successful, the Internet Archive's project would be a boon to students, journalists, or anyone who wants to check the references of a Wikipedia entry. Google Books also has a massive collection of digitized print books, but it tends to only show small snippets of a text. "I've tried to verify Wikipedia pages by searching blurbs in Google Books but it's an unpredictable link, and you often don't have enough surrounding context to evaluate the use," says Mike Caulfield, a digital literacy expert and director of blended and networked learning at Washington State University Vancouver. "The ability to read a page or two of context around a quote is crucial to both editors trying to protect the integrity of articles, and to readers who need to get to that next step of verification." You could, of course, verify the information the traditional way by tracking down a physical copy of a book. But students working late into the night on term papers, or reporters on tight deadlines, might not have time to order a book on Amazon or wait for a library book to become available. In other cases, books might be hard to come by. The Wikipedia entry on the internment of Japanese-Americans during World War II , for example, cites hard-to-find titles, says Internet Archive director of partnerships Wendy Hanamura. But thanks to the Internet Archive's Digital Library of Japanese-American Incarceration , created with the Seattle-based organization Densho , many of those rare books are now available online. The Internet Archive embarked on its effort to weave digital books into Wikipedia after the 2016 election. "No matter who you wanted to be president, I would say almost everyone would agree the whole process was a train wreck," Internet Archive founder Brewster Kahle said in a speech in San Francisco last week. From fake news and inauthentic social media campaigns waged by foreign nations to concerns about voting systems themselves being rigged, there were plenty of ways that technology and information systems failed the public. So Kahle convened a group of people to discuss how to improve the information ecosystem. One issue that came up was the fragility of Wikipedia citations. Books and academic journals supply some of the best, most reliable information for Wikipedia editors, but those sources frequently are either unavailable online or are behind paywalls. And even freely available internet content often disappears. The Internet Archive was in a unique position to help solve this problem. The organization's Wayback Machine service has archived 387 billion webpages since 2001. It's also been digitizing physical books and other analog media, and has now scanned 3.8 million books. It has millions more books warehoused. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Graham and company created the InternetArchiveBot, a tool that scans Wikipedia for broken links and automatically adds links to versions archived in the Wayback Machine. Because automatic editing tools require special permission to use, Graham has to work with the Wikipedia communities that manage versions of the encyclopedia in different languages. "All told, we've edited 14 million links; more than 11 million point to Internet Archive," he says. Adding links to books is similar but more challenging. "If a book has an ISBN number and an entry has a traditional citation format, it's pretty easy," Graham explains. But not all books have ISBN numbers, and many Wikipedia citations aren't properly formatted. For instance, some only cite the book and not a specific page number. There can also be differences between different editions of a book. Of course, the Internet Archive hasn’t scanned all the books cited by Wikipedia yet. It’s working hard to digitize collections from libraries around the world, along with donations from companies like Better World Books. Graham says the organization scans more than 1,000 books per day. 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1,945	2,017	"AI Research Is in Desperate Need of an Ethical Watchdog \| WIRED"	"https://www.wired.com/story/ai-research-is-in-desperate-need-of-an-ethical-watchdog"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Sophia Chen Science AI Research Is in Desperate Need of an Ethical Watchdog Getty Images Save this story Save Save this story Save About a week ago, Stanford University researchers posted online a study on the latest dystopian AI : They'd made a machine learning algorithm that essentially works as gaydar. After training it with tens of thousands of photographs from dating sites, the algorithm could perform better than a human judge in specific instances. For example, when given photographs of a gay white man and a straight white man taken from dating sites, the algorithm could guess which one was gay more accurately than actual people participating in the study.* The researchers’ motives? They wanted to protect gay people. “[Our] findings expose a threat to the privacy and safety of gay men and women,” wrote Michal Kosinski and Yilun Wang in the paper. They built the bomb so they could alert the public about its dangers. Alas, their good intentions fell on deaf ears. In a joint statement, LGBT advocacy groups Human Rights Campaign and GLAAD condemned the work , writing that the researchers had built a tool based on “junk science” that governments could use to identify and persecute gay people. AI expert Kate Crawford of Microsoft Research called it “AI phrenology” on Twitter. The American Psychological Association, whose journal was readying their work for publication, now says the study is under “ethical review.” Kosinski has received e-mail death threats. But the controversy illuminates a problem in AI bigger than any single algorithm. More social scientists are using AI intending to solve society’s ills, but they don’t have clear ethical guidelines to prevent them from accidentally harming people, says ethicist Jake Metcalf of Data & Society. “There aren’t consistent standards or transparent review practices,” he says. The guidelines governing social experiments are outdated and often irrelevant—meaning researchers have to make ad hoc rules as they go. Right now, if government-funded scientists want to research humans for a study, the law requires them to get the approval of an ethics committee known as an institutional review board, or IRB. Stanford’s review board approved Kosinski and Wang’s study. But these boards use rules developed 40 years ago for protecting people during real-life interactions, such as drawing blood or conducting interviews. “The regulations were designed for a very specific type of research harm and a specific set of research methods that simply don’t hold for data science,” says Metcalf. For example, if you merely use a database without interacting with real humans for a study, it’s not clear that you have to consult a review board at all. Review boards aren’t allowed to evaluate a study based on its potential social consequences. “The vast, vast, vast majority of what we call ‘big data’ research does not fall under the purview of federal regulations,” says Metcalf. So researchers have to take ethics into their own hands. Take a recent example: Last month, researchers affiliated with Stony Brook University and several major internet companies released a free app, a machine learning algorithm that guesses ethnicity and nationality from a name to about 80 percent accuracy. They trained the algorithm using millions of names from Twitter and from e-mail contact lists provided by an undisclosed company—and they didn't have to go through a university review board to make the app. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The app, called NamePrism , allows you to analyze millions of names at a time to look for society-level trends. Stony Brook computer scientist Steven Skiena , who used to work for the undisclosed company, says you could use it to track the hiring tendencies in swaths of industry. “The purpose of this tool is to identify and prevent discrimination,” says Skiena. Skiena's team wants academics and non-commercial researchers to use NamePrism. (They don’t get commercial funding to support the app’s server, although their team includes researchers affiliated with Amazon, Yahoo, Verizon, and NEC.) Psychologist Sean Young , who heads University of California’s Institute for Prediction Technology and is unaffiliated with NamePrism, says he could see himself using the app in HIV prevention research to efficiently target and help high-risk groups, such as minority men who have sex with men. More on AI WIRED Video Nicholas Thompson medicine Bahar Gholipour Artificial Intelligence Tom Simonite But ultimately, NamePrism is just a tool, and it’s up to users how they wield it. “You can use a hammer to build a house or break a house,” says sociologist Matthew Salganik of Princeton University and the author of Bit by Bit: Social Research In The Digital Age. “You could use this tool to help potentially identify discrimination. But you could also use this tool to discriminate.” Skiena’s group considered possible abuse before they released the app. But without having to go through a university IRB, they came up with their own safeguards. On the website, anonymous users can test no more than a thousand names per hour, and Skiena says they would restrict users further if necessary. Researchers who want to use the app for large-scale studies have to ask for permission from Skiena. He describes the approval process as "fairly ad hoc." He has refused access to businesses and accepted applications from academics affiliated with established institutions who have proposed "what seem to be reasonable topics of study." He also points out that names are public data. The group also went through an ethics review at the company that provided training list of names, although Metcalf says that an evaluation at a private company is the “weakest level of review that they could do." That's because the law does not require companies to follow the same regulations as publicly-funded research. “It’s not transparent at all to you or me how [the evaluation] was made, and whether it’s trustworthy,” Metcalf says. But the problem isn’t about NamePrism. “This tool by itself is not likely to cause a lot of harm,” says Metcalf. In fact, NamePrism could do a lot of good. Instead, the problem is the broken ethical system around it. AI researchers—sometimes with the noblest of intentions—don’t have clear standards for preventing potential harms. “It’s not very sexy,” says Metcalf. “There’s no Skynet or Terminator in that narrative.” Metcalf, along with researchers from six other institutions, has recently formed a group called Pervade to try to mend the system. This summer, they received a three million dollar grant from the National Science Foundation, and over the next four years, Pervade wants to put together a clearer ethical process for big data research that both universities and companies could use. “Our goal is to figure out, what regulations are actually helpful?” he says. But before then, we’ll be relying on the kindness—and foresight—of strangers. *Correction at 1:26 p.m. on 9/19/2017: An earlier version of this story misstated the accuracy of the Stanford algorithm. 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1,946	2,019	"I Opted Out of Facial Recognition at the Airport—It Wasn't Easy \| WIRED"	"https://www.wired.com/story/opt-out-of-facial-recognition-at-the-airport"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Allie Funk Security I Opted Out of Facial Recognition at the Airport—It Wasn't Easy Stephanie Yeow/AP Save this story Save Save this story Save The announcement came as we began to board. Last month, I was at Detroit’s Metro Airport for a connecting flight to Southeast Asia. I listened as a Delta Air Lines staff member informed passengers that the boarding process would use facial recognition instead of passport scanners. Allie Funk is a research analyst for Freedom on the Net , Freedom House's annual country-by-country assessment of internet freedom. She focuses on developments in the US and Asia. As a privacy-conscious person, I was uncomfortable boarding this way. I also knew I could opt out. Presumably, most of my fellow fliers did not: I didn't hear a single announcement alerting passengers how to avoid the face scanners. To figure out how to do so, I had to leave the boarding line, speak with a Delta representative at their information desk, get back in line, then request a passport scan when it was my turn to board. Federal agencies and airlines claim that facial recognition is an opt-out system, but my recent experience suggests they are incentivizing travelers to have their faces scanned—and disincentivizing them to sidestep the tech—by not clearly communicating alternative options. Last year, a Delta customer service representative reported that only 2 percent of customers opt out of facial-recognition. It's easy to see why. As I watched traveler after traveler stand in front of a facial scanner before boarding our flight, I had an eerie vision of a new privacy-invasive status quo. With our faces becoming yet another form of data to be collected, stored, and used, it seems we’re sleepwalking toward a hyper-surveilled environment, mollified by assurances that the process is undertaken in the name of security and convenience. I began to wonder: Will we only wake up once we no longer have the choice to opt out? Until we have evidence that facial recognition is accurate and reliable—as opposed to simply convenient—travelers should avoid the technology where they can. The facial recognition plan in US airports is built around the Customs and Border Protection Biometric Exit Program , which utilizes face-scanning technology to verify a traveler’s identity. CBP partners with airlines—including Delta, JetBlue, American Airlines, and others—to photograph each traveler while boarding. That image gets compared to one stored in a cloud-based photo-matching service populated with photos from visas, passports, or related immigration applications. The Biometric Exit Program is used in at least 17 airports, and a recently-released Department of Homeland Security report states that CBP anticipates having the ability to scan the faces of 97 percent of commercial air passengers departing the United States by 2023. This rapid deployment of facial recognition in airports follows a 2017 executive order in which President Trump expedited former President Obama’s efforts to use biometric technology. The Transportation Security Administration has since unveiled its own plan to improve partnership with CBP and to introduce the technology throughout the airport. The opportunity for this kind of biometric collection infrastructure to feed into a broader system of mass surveillance is staggering, as is its ability to erode privacy. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Proponents of these programs often argue that facial recognition in airports promotes security while providing convenience. But abandoning privacy should not be a prerequisite for achieving security. And in the case of technology like facial recognition, the “solution” can quickly become a deep and troubling problem of its own. For starters, facial recognition technology appears incapable of treating all passengers equally at this stage. Research shows that it is particularly unreliable for gender and racial minorities: one study , for example, found a 99 percent accuracy rate for white men, while the error rate for women who have darker skin reached up to 35 percent. This suggests that, for women and people of color, facial recognition could actually cause an increase in the likelihood to be unfairly targeted for additional screening measures. Americans should be concerned about whether images of their faces collected by this program will be used by companies and shared across different government agencies. Other data collected for immigration purposes—like social media details—can be shared with federal, state, and local agencies. If one government agency has a database with facial scans, it would be simple to share the data with others. This technology is already seeping into everyday life, and the increased regularity with which Americans encounter facial recognition as a matter of course while traveling will reinforce this familiarity; in this context, it is easy to imagine content from a government-operated facial recognition database being utilized in other settings aside from airports—say, for example, monitoring peaceful protests. There are also serious concerns about CBP’s storage of this data. A database with millions of facial scans is extremely sensitive, and breaches seem inevitable. Indeed, CBP officials recently revealed that thousands of photos of people’s faces and license plates were compromised after a cyberattack on a federal subcontractor. Once this sort of data is made insecure, there is no hope of getting it back. One cannot simply alter their face like they can their phone number or email address. Importantly, there have been some efforts to address facial recognition in airports. The government’s Privacy and Civil Liberties Oversight Board recently announced an aviation-security project to assess privacy and civil liberties implications with biometric technologies. Members of Congress have also shared similar concerns. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Nevertheless, the Biometric Exit Program needs to be stopped until it prioritizes travelers’ privacy and resolves its technical and legal shortcomings. At the state and local level, public opposition has driven cities and states to consider—and, in some cases, enact —restrictions on the use of facial recognition technology. The same healthy skepticism should be directed toward the technology’s deployment at our airports. Congress needs to supplement pressure from travelers with strong data protection laws that provide greater transparency and oversight. This should include strict limits on how long companies and government agencies can retain such intimate data. Private companies should not be allowed to utilize data collected for business purposes, and federal agencies should not be able to freely share this data with other parts of government. Policymakers should also ensure that biometric programs undergo thorough and transparent civil rights assessments prior to implementation. Until measures like these are met, travelers should be critical when submitting to facial recognition technology in airports. Ask yourself: Is saving a few minutes worth handing over your most sensitive biometric information? WIRED Opinion publishes pieces written by outside contributors and represents a wide range of viewpoints. Read more opinions here. Submit an op-ed at [email protected]. A device to detect “aggression” in schools often misfires Disney's new Lion King is the VR-fueled future of cinema Google Photos hacks to tame your picture overload It's time to switch to a privacy browser YouTube's “shitty robot” queen made a Tesla pickup truck 📱 Torn between the latest phones? Never fear—check out our iPhone buying guide and favorite Android phones 📩 Hungry for even more deep dives on your next favorite topic? 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1,947	2,019	"China's AI Unicorns Can Spot Faces. Now They Need New Tricks \| WIRED"	"https://www.wired.com/story/chinas-ai-unicorns-spot-faces-new-tricks"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Will Knight Business China's AI Unicorns Can Spot Faces. Now They Need New Tricks Xu Li, CEO of China's SenseTime, which recently deployed a facial-recognition system at Beijing’s new Daxing airport for China Eastern airlines. Photograph: Gilles Sabrie/Bloomberg/Getty Images Save this story Save Save this story Save Application Face recognition End User Big company Sector IT Manufacturing Public safety Technology Machine learning Robotics A warehouse in an industrial park about an hour’s drive north of downtown Beijing offers a paradoxical picture of China’s much-hyped, and increasingly controversial, artificial intelligence boom. Inside the building, a handful of squat cylindrical robots scuttle about, following an intricate and invisible pattern. Occasionally, one zips beneath a stack of shelves, raises it gently off the ground, then brings it to a station where a human worker can grab items for packing. A handful of engineers stare intently at code running on a bank of computers. The robots and the AI behind them were developed by Megvii , one of China’s vaunted AI unicorns. The impressive demo might seem like further evidence of China’s AI prowess—perhaps even proof that the country is poised to eclipse the US in this critical area. But the warehouse also points to a fundamental weakness with China’s AI. Amazon has been using similar technology in US fulfillment centers for several years. China’s AI champions have spun AI algorithms into gold in recent years, but that may become more difficult as the technology becomes more widely available. Megvii, a private company that CB Insights says is valued at around $4 billion, hopes to convince customers to buy its warehouse and manufacturing AI technology as it looks to move beyond a business built largely around facial-recognition technology. The trouble is, AI is not yet proven as a general-purpose technology that can easily be applied to different industries. Broader challenges, including newly imposed US trade restrictions, will make things even more difficult. “These companies are not going to be big companies, like Alibaba or Tencent,” predicts Nina Xiang, a business journalist in Hong Kong and author of Red AI , a recent book about China’s AI boom. “They will remain small operators, and some valuations will have to be corrected.” In October, Megvii and five other AI-focused Chinese companies were added to a US export blacklist , because Chinese authorities allegedly use their technology to monitor and control Muslim minorities in Xinjiang, a province in western China. The blockade means these companies can no longer buy crucial components such as advanced microchips from US firms. China’s AI boom has produced more than a dozen unicorns, private companies valued at more than $1 billion. These include SenseTime , valued at $7.5 billion, the company’s CEO told Bloomberg earlier this year, and Yitu and CloudWalk, both valued above $2 billion. Another prominent AI company, iFlytek , has been around longer, having started out making speech-recognition tech, and it carries a market capitalization of $10 billion on the Shenzhen stock exchange. “The largest problem these companies face may be the dawning realization on investors that, although it seems promising, in most areas AI just isn't ready for the big time.” Helen Toner, Georgetown University Megvii, which filed to go public on the Hong Kong stock exchange in September, has genuinely impressive AI expertise, having developed core algorithms and software. It was founded by several graduates of a renowned AI program at Tsinghua University in Beijing. The company’s IPO filing offers a rare insight into the finances of a Chinese AI giant, and highlights just how dependent the company seems to be on face recognition and surveillance for now. Revenue grew four-fold last year to $200 million, compared with 2017; but its “City IoT” segment, which encompasses surveillance and security systems, accounts for nearly three-quarters of that revenue. State-led development may be both a blessing and a curse for China’s AI enterprises. When the government announced a grand national AI plan in July 2017, it served as a signal for Chinese cities and provinces to pour money into AI projects. Xiang says Megvii and other Chinese AI unicorns seem to be heavily reliant on government contracts, subsidies, and other forms of strategic support. “On average, we can say a significant share of these companies’ revenues is government-reliant,” she says. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The Chinese AI companies have made efforts to move into new areas over the past few years. Besides Megvii’s move into logistics and manufacturing, Yitu touts its work in medical imaging and document analysis, SenseTime is investing in autonomous driving, and iFlytek often demos tools for analyzing legal documents. The catch is that AI is relatively unproven in such areas, and it’s unclear how much revenue the companies have generated from these ventures. “Applying AI to business requires skills that are more artful,” says Qiang Yang , a professor at the Hong Kong University of Science and Technology and chief AI officer at WeBank, a banking startup founded by Tencent. He says a company needs to understand how to use AI tools to solve real-world problems, how to gather sufficient high-quality data, and how these challenges fit into the business life cycle. “This is hard,” Yang adds. “The largest problem these companies face may be the dawning realization on investors that, although it seems promising, in most areas AI just isn't ready for the big time,” says Helen Toner, of the Center for Security and Emerging Technology at Georgetown University, who has studied the development of AI in China. There’s a technical reason for the predicament. Chinese AI companies built early success by applying deep learning, an AI technique that has dramatically improved machine perception in recent years, to problems like facial and speech recognition. Now, as deep learning becomes more broadly accessible through software packages and APIs, these companies need to expand into areas that require greater domain expertise. Facial recognition has been particularly lucrative for Chinese companies, and the technology is widely used across the country. A report issued by IHS Market last week concludes that a billion surveillance cameras will be in operation worldwide by 2021, with about half of them in China. SenseTime, for example, recently deployed a system at Beijing’s new Daxing airport for China Eastern airlines. This uses facial recognition to let passengers check in, pass through security, enter the business lounge, and even board a plane without showing a boarding pass. Megvii’s facial-recognition technology lets people unlock phones made by Oppo, Xiaomi, and Vivo and log into apps with a glance; it’s also bundled with security cameras that automatically check employees into office buildings. Like other Chinese AI companies, Megvii also supplies this technology to police departments that use it to hunt for criminals in surveillance footage. The company’s tech was being used by the authorities in Xinjiang, although Megvii says a developer used its application programming interface without the company’s knowledge. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Megvii declined to comment, citing a quiet period around its planned IPO. (The warehouse demo took place earlier.) Kang Ho, a spokesperson for SenseTime, challenged the idea that facial-recognition technology is now more broadly available. Still, Kang pointed to a range of ongoing projects in other fields, including tools for medical imaging, education, and virtual reality. There are other signs that China’s AI bonanza, supposedly built on huge quantities of data and government backing, may be less spectacular than often assumed. A report published last week by the analyst firm IDC and Qbitai, a Chinese media company, found that 60 percent of executives surveyed expect significant difficulty deploying AI due to poor-quality data and a scarcity of AI talent. Andrew Grotto , a professor at Stanford who coauthored a recent report on the financial details of China’s AI industry, agrees that these AI unicorns face significant challenges. Their true value “is a topic of debate in China,” he says. China’s crop of well-funded AI-centric companies is unusual. The US’ big AI players, like Google, Facebook, Amazon, and Microsoft, all have existing businesses, like advertising, ecommerce, or software licensing, to bankroll their AI efforts. And while China’s own tech powerhouses—Alibaba, Tencent, and Baidu—are also heavily invested heavily in AI, the country’s tech market has been flooded by companies touting AI itself as a business in recent years. “Some Chinese firms, like Tencent, are among the very best in the world,” Grotto says. “But there are a whole lot of pretenders, too.” The hype around Chinese AI certainly seems to have backfired if it helped prompt the US government’s export ban on Megvii and others. The White House appears concerned that China could soon steal an advantage in this critical area of technology. It would be helpful for Megvii and others to diversify away from surveillance technologies now under scrutiny, says Rebecca Fannin, author of Tech Titans of China. Fannin adds that becoming less reliant on the West for advanced technology will benefit China long-term, but “could be a challenge” for the targeted AI companies. Even if Megvii can build a major new business supplying AI-powered robots to manufacturers and ecommerce companies—and even if other Chinese AI companies find their own successes—a technological “decoupling” of China and America may affect companies in both countries in unforeseen ways. The latest tit-for-tat measure saw the Chinese government order this week that US computers and software be replaced with Chinese technology in official buildings over the next few years. Whether or not a trade agreement is reached between China and the US, the emergence of a more cautious and uneasy relationship seems inevitable. “Beijing’s announcement is a harbinger of things to come,” says Grotto of Stanford. Why the “queen of shitty robots” renounced her crown Amazon, Google, Microsoft— who has the greenest cloud ? Instagram, my daughter, and me Ewoks are the most tactically advanced fighting force in Star Wars Everything you need to know about influencers 👁 Will AI as a field "hit the wall" soon ? Plus, the latest news on artificial intelligence 🏃🏽‍♀️ Want the best tools to get healthy? Check out our Gear team’s picks for the best fitness trackers , running gear (including shoes and socks ), and best headphones. Senior Writer X Topics artificial intelligence face recognition machine learning China Will Knight Will Knight Peter Guest Will Knight Amanda Hoover Khari Johnson Paresh Dave Khari Johnson Facebook X Pinterest YouTube Instagram Tiktok More From WIRED Subscribe Newsletters Mattresses Reviews FAQ Wired Staff Coupons Black Friday Editorial Standards Archive Contact Advertise Contact Us Customer Care Jobs Press Center RSS Accessibility Help Condé Nast Store Do Not Sell My Personal Info © 2023 Condé Nast. All rights reserved. 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1,948	2,019	"Why Chinese Companies Plug a US Test for Facial Recognition \| WIRED"	"https://www.wired.com/story/china-earns-high-marks-us-test-facial-recognition"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Tom Simonite Business Why Chinese Companies Plug a US Test for Facial Recognition STR/Getty Images Save this story Save Save this story Save Application Face recognition Company Amazon Microsoft End User Government Sector Public safety Source Data Images Technology Machine vision Last year, Chinese police arrested a man at a pop concert after he was flagged as a criminal suspect by a facial recognition system installed at the venue. The software that called the cops was developed by Shanghai startup Yitu Tech. It was marketed with a stamp of approval from the US government. Yitu is a top performer on a testing program run by the National Institute of Standards and Technology that’s vital to the fast-growing facial recognition industry. More than 60 companies took part in the most recent rounds of testing. The rankings are dominated by entrants from Russia and China, where governments are bullish about facial recognition and relatively unconcerned about privacy. “It’s considered the industry standard and users rely on NIST’s benchmark for their business decisions and purchases,” says Shuang Wu, a Yitu research scientist and head of Yitu’s Silicon Valley outpost. “Both Chinese and international customers ask about it.” Yitu’s technology is in use by police and at subway stations and ATMs. It’s currently ranked first on one of NIST’s two main tests, which challenges algorithms to detect when two photos show the same face. That task is at the heart of systems that check passports or control access to buildings and computer systems. The next five best-performing companies on that test are Russian or Chinese. When the State Department last June picked Paris-based Idemia to provide software used to screen passport applications, it said it had chosen “the most accurate non-Russian or Chinese software” to manage the 360 million faces it has on file. In a subsequent round of tests, US startup Ever AI ranked seventh, making it the top-performing company outside Russia and China. “Ever since the NIST results came out there’s been a pretty steady stream of customers,” including new interest from government agencies, says Doug Aley, Ever AI’s CEO. NIST is an arm of the US Commerce Department with the mission of promoting US competitiveness by advancing the science of measurement. Its Facial Recognition Vendor Test program began in 2000, with the support of the Pentagon, after numerous US agencies became interested in using the technology. Since then, NIST has tracked the steady improvement in algorithms designed to scrutinize human physiognomy, and developed new testing regimes to keep up. The agency now tests algorithms in a subterranean computer room in Gaithersburg, Maryland, using millions of anonymized mugshots and visa photos sourced from government agencies. Its results show that accuracy has improved significantly since the emergence of the neural network technology driving the tech industry’s current AI obsession. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The other NIST test simulates the way facial recognition is used by police investigators, asking algorithms to search for a specific face in a sea of many others. In 2010, the best software could identify someone in a collection of 1.6 million mugshots about 92 percent of the time. In a late 2018 version of that test the best result was 99.7 percent, a nearly 30-fold reduction in error rate. The best performer on that test is Microsoft, which was scored by NIST for the first time in November. The next three best entrants were Russian and Chinese, with Yitu fourth. Ever AI came fifth. Of the more than 60 entrants listed in NIST’s most recent reports whose home base could be identified, 13 were from the US, 12 from China, and 7 from Russia. For companies outside of Russia and China, doing well on NIST’s rankings opens the door to contracts with the US government. “Federal agencies don’t make buying decisions without checking with NIST,” says Benji Hutchinson, vice president of federal operations at NEC. The company has facial recognition contracts with the departments of State, Homeland Security, and Defense, and its technology is being tested to check the identity of international passengers at several US airports. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Microsoft president Brad Smith touted the company’s new NIST results in a December blog post that called for federal regulations on the technology and highlighted the importance of independent testing. The company declined to answer queries about its decision to enter the program and interest in government facial recognition contracts but defended government use of the technology in recent testimony opposing a Washington State bill that would restrict facial recognition. IBM and Amazon both sell facial recognition to local US law enforcement agencies, but neither has submitted its technology to NIST’s testing. Amazon said in January it respects NIST’s test but that its technology is deeply integrated with Amazon’s cloud computing platform and can’t be sent off to Gaithersburg for the agency to test on its own computers. IBM computer vision research manager John Smith said the company was working with NIST to broaden its testing of how well facial recognition works across different demographics before deciding whether to take part. Tech companies and their critics have become more concerned about demographic bias in facial recognition after experiments showed that Amazon’s technology made more errors on black faces and that facial analysis software from IBM and Microsoft was less accurate for women with darker skin. Amazon disputes the findings, and Microsoft and IBM say they have upgraded their systems. Os Keyes, a researcher at the University of Washington, says findings like those help show that facial recognition must be scrutinized more broadly than through lab tests of accuracy. Keyes published a paper last year criticizing NIST and others for contributing to the development of gender recognition software that doesn’t account for trans people, potentially causing problems for an already marginalized group. A 2015 NIST report on testing gender recognition software suggested that the technology could be used in alarm systems for women's bathrooms or locker rooms to alert if a man enters. “NIST needs to employ ethicists or sociologists or qualitative researchers that could go out and look at the impact of these technologies,” Keyes says. Patrick Grother, one of the NIST scientists leading the testing exercise, says his group is expanding its testing of demographic differences in facial recognition technology and helping address potential flaws in the technology in its own way. Although discussion of racial and gender bias has grown, more work is needed on figuring out how to test and measure it, Grother says, adding that NIST can help the industry address any problems by advancing the science of detecting and tracking them. “We try and bring sunlight and oxygen to the marketplace.” President Trump appears to want NIST to take a more active role in sustaining the development of artificial intelligence. An executive order he signed last month to encourage AI development in the US directed the agency to develop standards and tools to encourage “reliable, robust, and trustworthy” AI systems. What's the value of a Facebook cryptocoin ? Quantum physics could (maybe) save the grid from hacks Shoot super-smooth video with DJI's Osmo Pocket No, data is not the new oil— and never will be Amazon Alexa and the search for the one perfect answer 👀 Looking for the latest gadgets? Check out our latest buying guides and best deals all year round 📩 Want more? 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1,949	2,022	"Why It's So Hard to Count Twitter Bots \| WIRED"	"https://www.wired.com/story/twitter-musk-bots"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Will Knight Business Why It's So Hard to Count Twitter Bots Photograph: MirageC/Getty Images Save this story Save Save this story Save Application Content moderation End User Big company Research Sector Social media Source Data Clickstream Text Technology Machine learning Is the Twitter account @ElonMusk a bot? One of the best algorithms for detecting fake accounts thinks it might be , which shows how challenging it is to quantify the proportion of fake accounts across the social network. Counting Twitter bots has become a point of contention in Elon Musk ’s ongoing $44 billion acquisition of Twitter. Last Friday, the billionaire tweeted that he was putting his purchase “temporarily on hold” until the company provided details to back up its claim (as stated in its latest SEC filing ) that fewer than 5 percent of “monetizable daily active users” on Twitter are spam or fake. Musk also outlined a plan to count bots himself that involved sampling 100 @Twitter followers to see how many were bots and said the approach suggests over 20 percent of accounts are fake. But accurately quantifying the percentage of bots on Twitter is a lot more difficult, according to experts. Finding them isn’t hard if you know where to look. Certain accounts, including Musk’s, seem to attract plenty of them. “If you simply mention Elon Musk on Twitter, you immediately get engaged with a ton of crypto bots,” says Chris Bail , a professor of sociology at Duke University who studies social media. Twitter is not the only social network to struggle with fake accounts. Facebook removes billions of bogus accounts every year. But it is hard to know for certain that an account on Twitter is a bot, since legitimate users may have few followers, rarely tweet, or have strange usernames. It is even more difficult to gauge the number of bots that operate across the platform as a whole. To test Musk’s proposed methodology , IV.ai , an AI company, looked at 100 accounts that follow Musk’s car manufacturing company Tesla on Twitter. An algorithmic examination of the accounts on Tuesday found that more than 20 accounts out of 100 have a high likelihood of being bots. A manual examination of the same 100 concluded that more than half may be bots. And an analysis of the topics discussed by those accounts did not find evidence that any of the suspected accounts were promotional. But many of those accounts also disappeared shortly after, suggesting that Twitter catches bots fairly quickly. Vince Lynch , CEO of IV.ai, says identifying dubious accounts is also inherently subjective and involves a degree of uncertainty. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg “It’s a very hard problem,” says Filippo Menczer , a professor at Indiana University who led the development of the Botometer algorithm , which gave Musk’s account a relatively high bot score. Menczer says that looking at 100 accounts will not be representative of Twitter’s daily active users, and different samples will produce wildly different results. “I want to hope that that was a joke,” Menczer says of the methodology. Automated accounts have become more sophisticated and complex in recent years. Many fake accounts are partly operated by humans, as well as machines, or just amplify messages written by real people (what Menczer calls “cyborg accounts”). Other accounts use tricks designed to evade human and algorithmic detection, such as rapidly liking and unliking tweets or posting and deleting tweets. And of course there are plenty of automated or semi-automated accounts, such as those run by many companies, that aren’t actually harmful. The Botometer algorithm uses machine learning to assess a wide range of public data tied to an account—not just the content of tweets, but when messages are sent, who follows an account, and so on—to determine the likelihood of it being a bot. Although the algorithm is state of the art, Menczer says, “a lot of accounts now fall to the range where the algorithm is basically not very sure.” Menczer and others say that spotting bots is a game of cat and mouse. But they add that it may become significantly more challenging in the future as spammers use algorithms that are better able to generate convincing text and hold coherent conversations. Twitter itself is better equipped to spot bots using machine learning because it has access to a lot more data about each account. This includes a user’s full history of activity, as well as the different IP addresses and devices they use. But Delip Rao , a machine learning expert who worked on spam detection at Twitter from 2011 to 2013, says the company may not be able to reveal how this works because doing so could disclose personal data or information that could be used to manipulate the platform’s recommendation system. This week, Musk also got into a spat with Parag Agrawal, Twitter’s CEO, over how easily the company could disclose its methodology for finding bots. On Monday, Agrawal posted a thread explaining how complex the challenge still is. He noted that the private data Twitter holds may change calculations around the number of bots on the service. “FirstnameBunchOfNumbers with no profile pic and odd tweets might seem like a bot or spam to you, but behind the scenes we often see multiple indicators that it’s a real person,” he wrote in the thread. Agrawal also said that Twitter could not disclose details of these assessments. If Twitter is unable, or unwilling, to reveal its methodology and Musk says he won’t proceed without details, the deal may remain in limbo. Of course, Musk could be using the issue as leverage to negotiate the price down. For now, Musk seems dissatisfied with Twitter’s efforts to explain why finding bots is not as easy as he thinks. He responded to Agrawal’s long thread on Monday with a simple message that seemed far more fitting for a bot than a prospective buyer of Twitter: a single, smiling poop emoji. Update 5/9/2022 12:00 ET: This piece has been updated to not imply that IV.ai singlehandedly identified bot-like activity among accounts amplifying misinformation about US voter fraud. 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1,950	2,021	"How to Solve Captchas—and Why They've Gotten So Hard \| WIRED"	"https://www.wired.com/story/im-not-a-robot-why-captchas-hard-to-solve"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Sharon Waters Security I’m Not a Robot! So Why Won’t Captchas Believe Me? Everyone's run into a Captcha they just can't get right. But there are some strategies that can help. Photograph: Getty Images Save this story Save Save this story Save Application Identifying Fabrications Content moderation Human-computer interaction Company Alphabet Google End User Consumer Big company Small company Sector Consumer services Source Data Images Technology Machine learning Machine vision Like so many this winter, Norine McMahon was searching for a Covid-19 vaccine appointment, hitting Refresh on her browser continuously. The Washington, DC, resident was elated to find an opening in late February, but delight turned to disappointment when she failed the captcha user-verification test, even though she swore she entered the letters and numbers correctly. “Then I would do it really slowly to make sure I was getting it correct, because of course the pressure is on. It happened a dozen times. The captchas weren’t working,” says McMahon, 61, a facilities director who gave up that day but eventually secured an appointment. The captcha chaos with DC Health’s portal was one of several technical problems widely reported at the time. But captchas have been frustrating users since long before the pandemic. “Captcha” stands for “completely automated public Turing test to tell computers and humans apart.” The Turing test was created in 1950 by Alan Turing , a British mathematician considered a founding father of artificial intelligence, to help determine whether a computer can demonstrate intelligent behavior similar to a person. Turing called it the “imitation game.” Luis von Ahn helped develop the modern captcha as a grad student at Carnegie Mellon University, where he is now a consulting professor, and later invented reCaptcha, which Google acquired. The goal of captcha is to create tests or puzzles that humans can solve but bots can't—so you, a mere mortal, might have a shot at a decent seat to a Springsteen concert when they go on sale at 10:00.01 am. It can be a tricky balance, especially as machines become more sophisticated. “Usually artificial intelligence systems are capable of coping better than humans because, as an example, they don’t suffer from annoyance. They are infinitely patient, they don’t care about wasting time,” says Mauro Migliardi, associate professor at the University of Padua in Italy. He recently coauthored a paper summarizing 20 years of captcha versions and their effectiveness. Google won’t say what share of the captcha market it has, but it appears dominant in the US, with the reCaptcha name seen frequently on various sites. For this story, Google requested that questions be submitted in writing and then answered them in writing, saying direct quotes could not be used. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Google has a reCaptcha “help” page , but its answers are underwhelming. One question asks, “This captcha is too hard,” to which Google answers, “Don’t worry. Some captchas are hard. Just click the reload button next to the image to get another one.” That help page also notes that Google uses captchas to train its AI, saying that the human effort that you and I put into solving them goes towards improving their products that digitize text, annotate images, and more. Google’s support page did not answer other questions so many ask, especially about the photo grid challenges. If there is a sliver of a bus in a square, do you have to click it? When selecting traffic lights, do you click the poles? When asked these questions, Google advised selecting the majority of squares that have the bus or traffic lights. Then there are the blurry photos, forcing users to move closer to the screen as they try to discern if there is a chimney in the fuzzy distance. Asked why the image reCaptchas are often blurry, Google said it works hard each day to reduce the number of captchas people need to solve, and says it improves its heuristics when it shows the “I'm not a robot” checkbox so that it doesn't show challenges to humans. Meriem Guerar, a researcher at the University of Genoa, Italy, offered a simpler explanation for the poor quality of the images. “The challenge presents often noisy and blurry images in order to make it harder to recognize, for bots using state-of-the-art image recognition technologies,” says Guerar, who has coauthored papers with Migliardi on captcha. “Noise, distraction of images, making them blurry, these are known as anti-recognition mechanisms.” Sometimes the captchas are flat-out wrong. Charles Bergquist said he felt a mix of amusement and frustration when he was asked to pick parking meters, was denied when he chose the one meter shown, and could only solve the puzzle by selecting the meter and two mailboxes. “It was frustrating that I couldn't get by it to get into the page that I wanted without feeding back incorrect information,” says Bergquist, who is director of the Science Friday radio program. Goofs like this are frequently discussed on social media or the /r/captcha Reddit group that finds comedy in bad captchas. Another Reddit group, /r/CaptchaArt , with nearly 20,000 members, incorporates captchas into cartoons and other art. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg Since captchas aren’t going away anytime soon, here are some tips to lower the frustration level with the captcha-solving process. It might be small comfort if you are stymied by a poor puzzle, but captchas are designed to protect the websites you visit, and ultimately you. “Alan Turing's captcha concept is, in itself, genius; but as the abilities of the robots become more sophisticated, captcha systems are becoming increasingly complex, leading to some very invasive user experiences,” says Matt Bliss, technical director at Bliss Digital in Hampshire, UK. After having to solve four captchas in a row, a frustrated Bliss redubbed the challenges as “complicated awkward patience test to tell crosswalks and hydrants apart.” As an architect and developer, Bliss understands the purpose behind captchas, and he notes that the free tools to use them are easy to implement by web designers and developers. “Unfortunately, this can lead to them being implemented as a cheap fix in situations where less invasive and more user-friendly approaches would be more appropriate, but would inevitably cost more to design and adopt,” he said. It may not feel like it, but captcha designers are trying to ease your pain. Google said it is continually working with its customers to find the best balance between user friction and stopping bots. Google’s reCaptcha product started as words that bots had a difficult time dissecting, then evolved to click boxes and crosswalks to defend from fraud, not just bots. The third version of reCaptcha has no user interaction, relying instead on behavioral analysis, so there is a frictionless user experience, according to Google. The fourth and latest version is reCaptcha Enterprise, which Google says offers unique capabilities built specifically for the enterprise and provides enhanced detection measures, such as extra-granular scores, reason codes for high-risk scores, and the ability to tune the risk analysis engine to the site’s specific needs. Recent advances in AI have made automated programs better at recognition tasks than humans, said Guerar. She and her team created an alternative called cappcha (the second P stands for “physical”) based on humans’ ability to perform physical tasks instead of solving difficult cognitive problems. Actions include tilting a smartphone or making micro-movements while typing on a laptop. “The rationale behind cappcha is that bots, which are pieces of code, cannot perform physical tasks,” says Guerar. “There are actions that only a human can do.” An outdated browser can trigger a verification challenge, according to Guerar and Migliardi. Keeping current on updates is another sign of humanity, the professors said. “A bot will probably not be careful in updating software,” said Migliardi. Google’s reCaptcha help page also recommends having JavaScript enabled in your browser and disabling plug-ins that might conflict with reCaptcha. If the website you are visiting uses Google’s reCaptcha, log into your Gmail account before looking for tickets or appointments, says Guerar. Also, allow cookies before you start searching, advises Guerar and Migliardi. ”Let them snoop,” said Migliardi. So-called invisible captchas work behind the scenes to verify you are human, and one way they do that is by analyzing your browsing history. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The professors noted the privacy concerns and suggested that people use tools to clear their history and remove cookies. But if quick access to a website is the priority, Migliardi recommends doing a short session of web surfing allowing cookies so Google already knows you’re a real person. “Just go through a few websites and let them give you all the cookies they want. That will probably show that you are human. Do the cleanup just after you get the reservation for your vaccine and stuff like that,” said Migliardi. “It’s a little bit like going to the emissions test with a warm engine.” Some captchas are tolerable, even interesting. While searching for NCAA tournament tickets in March, I discovered Ticketmaster was using a new version of captcha with a cartoon-y image overlaid with random items, such as a bicycle, T-shirt, and hammer, all of which had to be clicked in a specified order. Even though I was laser-focused on securing tickets to games at Hinkle Fieldhouse, I appreciated the new format and that the image wasn’t blurry. Ticketmaster, which last addressed captchas on its blog in 2014 , did not respond to multiple requests for information about its latest captchas, and Google said it is not one of its reCaptchas. Bergquist doesn’t mind when a captcha asks him to transcribe a line of handwritten text—“fancy ye olde script writing,” as he puts it—from a photo of an old document, such as a census record or ship manifest. “Those I actually find kind of cool. It’s a bit of escapism, and I get to think about what this person or thing actually was, way back when, and also I feel like it’s making something somewhere more accessible to somebody. Like, somebody is actually going to use that information at some point about the census or the ancient ship cargo or whatever and improve the world somehow,” Bergquist says with a laugh. 📩 The latest on tech, science, and more: Get our newsletters ! Everything you’ve heard about Section 230 is wrong Why not turn airports into giant solar farms ? Google gets serious about two-factor authentication. Good ! Schedule emails and texts to send anytime you want Help! 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1,951	2,019	"New Film Shows How Bellingcat Cracks the Web's Toughest Cases \| WIRED"	"https://www.wired.com/story/bellingcat-documentary-south-by-southwest"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Issie Lapowsky Security New Film Shows How Bellingcat Cracks the Web's Toughest Cases Bellingcat's team of researchers came into prominence tackling tough cases, like the downing of Malaysia Airlines flight 17 over Ukraine in 2014. Submarine Amsterdam 2018 Save this story Save Save this story Save Aric Toler’s face is illuminated only by the glow of the video playing on his laptop. It’s dashcam footage, supposedly captured by a driver in the town of Makiivka in eastern Ukraine, showing a Russian military convoy on its way to shoot down Malaysia Airlines flight 17 on July 17, 2014. At least, that’s the theory. Toler just has to prove it. To the untrained eye, the video is awfully dull. But to Toler, who’s part of a global team of digital detectives known as Bellingcat, it’s a goldmine. He trains his eyes on the screen and watches as, about 45 seconds in, a white Volvo truck enters the frame. It’s attached to a bright red trailer, which carries a Buk missile launcher bearing a resemblance to the one thought to have been used in the attack. There’s a Jeep following close behind, and a gas station to the right. A few seconds later, Toler presses pause, as the sign advertising the gas station’s prices comes into view. The gas station and the prices on the board, he explains, are both clues to where and when the video was filmed. Toler then navigates over to Google Earth, and drills down to the town of Makiivka. He finds the gas station from the video on the map, and then scrolls back in time to see the satellite image of that area taken on July 17, 2014. He zooms in, and sure enough, he spots the outline of a white truck with a heavy shadow behind it, making its way down the road. Not only that, but he can see the same cars in the gas station parking lot that appear in the video. As an extra bit of confirmation, Toler explains, the fuel prices on the sign in the video match historical records for the fuel prices at that very gas station on that very day. But perhaps the most chilling detail of all: In the video on Toler’s screen, the Buk is carrying four missiles. In another online video of the same convoy, taken the following day, it was carrying just three. “We know what happened to that missile,” Toler explains. "Anybody can do it if you want to; you can step into the role of Bellingcat." Director Hans Pool This is director Hans Pool’s favorite scene in his new documentary Truth in a Post Truth World , which will screen this Sunday at the South by Southwest film festival. The film tells the story of Bellingcat, a collective of investigative researchers, many of whom started as amateurs, who spend their off hours meticulously stringing together kernels of information they find online, in order to expose some of the world’s most notorious operatives. It was Bellingcat that blamed the Russian army for the downing of MH 17, years before European officials confirmed those findings. It was Bellingcat, in collaboration with the Russian outlet The Insider, that identified the men believed to have poisoned former Russian military officer Sergei Skripal in the UK in 2018. And it was Bellingcat that helped a group of online activists identify the assailants in a brutal attack at the Unite the Right rally in Charlottesville, Virginia, by scouring news and social media photos and obsessively mapping the constellation of moles on one guy's neck. They did it without the backing of a venerated news organization or the blessing of any government body. When Bellingcat’s founder Eliot Higgins first began uncovering secrets about Syria’s civil war back in 2012, he had no arms experience to speak of, didn’t know any Arabic, and published his blog, Brown Moses, from the comfort of his home, all while watching after his daughter. When CNN came to interview him in those early days, Higgins says sheepishly in the film, “They called me a stay-at-home Mr. Mom.” This is precisely what drew Pool to the Bellingcat crew. “For me, it was very interesting that house fathers are doing this kind of work,” Pool says. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg The scene with Toler, Pool says, showcases how the Bellingcat investigators go about proving or disproving every photo, video, or news story that crosses their paths. But perhaps even more importantly, it's also a reminder of how much deeply sensitive information is free for the taking online. In an information landscape increasingly polluted with propaganda, misinformation, and sophisticated spin, it's more important than ever to use that information to verify the stories we're being sold. That's the message at the crux of the film, says Pool. The media industry is undergoing a tectonic shift, where a handful of volunteer researchers can have just as much of an impact as any pedigreed reporter at the paper of record. "Anybody can do it if you want to; you can step into the role of Bellingcat," he says. Actually filming this work poses a cinematic challenge. "It’s five guys behind screens," Pool admits. Which is why the central drama of Truth in a Post Truth World isn't the relationship between the Bellingcat guys—they mostly communicate via Slack—or any one compelling character, but instead, what they find. "This documentary is an extension of the attempt to spread the word." Eliot Higgins, Bellingcat In one scene, Bellingcat investigator Christiaan Triebert explains how fake news spreads even among reputable news sources. He pulls up footage of what appears to be a chaotic street scene after a car bomb exploded in Baghdad in 2016. "Car bomb kills at least eight in Baghdad market," read the Reuters report, which cited police and medical staff. Soon, The Associated Press and The New York Times picked up the story, too, upping the body count to 10. The only problem: A day later, surveillance video appeared on Twitter, showing the same car exploding in the exact same place, only this time, the street was empty. It's only after the car explodes that a rush of people ran into view, dropped to the ground, and feigned injuries. Triebert deduced that the bombing, which had made international headlines, appeared to be staged. Triebert doesn't hold this against the media outlets and news wires who covered the bombing in real time. In fact, the film at one point shows Triebert working directly with The New York Times on an investigation. Instead, he says the traditional news industry and groups like Bellingcat have a mutually beneficial relationship. "If you never have wire reporting, we'd have nothing to go on," he says. At the time time, cash and time-strapped news organizations often lack the resources to follow the digital breadcrumbs wherever they may lead, the way a volunteer group like Bellingcat does. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg "Nine out of 10 times, we dig through stuff, and I will not find something," Triebert says. "How am I going to tell my boss I spent my month on 10 projects and nine projects were shit?" And so, it's in everyone's best interests—the media's, and certainly the public's—to have more people doing what Bellingcat is doing. That's why Bellingcat hosts training sessions for other wannabe investigators around the world, and makes its research toolkit publicly accessible. It's also why, Higgins says, he was interested in participating in the film in the first place. "A big part of Bellingcat's mission is to spread open-source investigations," he says. "This documentary is an extension of the attempt to spread the word." Truth in a Post Truth World will screen at South by Southwest on Sunday at 2:15 pm CST Inside the “black box” of a neural network Quantum physics could (maybe) save the grid from hacks Want a foldable phone? Hold out for real glass The Siberian city where the winter high is –40°F Will AI achieve consciousness? Wrong question 👀 Looking for the latest gadgets? Check out our latest buying guides and best deals all year round 📩 Want more? 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The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "
1,952	2,023	"Big AI Won’t Stop Election Deepfakes With Watermarks \| WIRED"	"https://www.wired.com/story/ai-watermarking-misinformation"	"Open Navigation Menu To revist this article, visit My Profile, then View saved stories. Close Alert Backchannel Business Culture Gear Ideas Science Security Merch To revist this article, visit My Profile, then View saved stories. Close Alert Search Backchannel Business Culture Gear Ideas Science Security Merch Podcasts Video Artificial Intelligence Climate Games Newsletters Magazine Events Wired Insider Jobs Coupons Vittoria Elliott Business Big AI Won’t Stop Election Deepfakes With Watermarks Illustration: themotioncloud/Getty Images Save this story Save Save this story Save In May, a fake image of an explosion near the Pentagon went viral on Twitter. It was soon followed by images seeming to show explosions near the White House as well. Experts in mis- and disinformation quickly flagged that the images seemed to have been generated by artificial intelligence, but not before the stock market had started to dip. It was only the latest example of how fake content can have troubling real-world effects. The boom in generative artificial intelligence has meant that tools to create fake images and videos, and pump out huge amounts of convincing text, are now freely available. Misinformation experts say we are entering a new age where distinguishing what is real from what isn’t will become increasingly difficult. Last week the major AI companies, including OpenAI, Google, Microsoft, and Amazon, promised the US government that they would try to mitigate the harms that could be caused by their technologies. But it’s unlikely to stem the coming tide of AI-generated content and the confusion that it could bring. The White House says the companies’ “voluntary commitment” includes “developing robust technical mechanisms to ensure that users know when content is AI generated, such as a watermarking system,” as part of the effort to prevent AI from being used for “fraud and deception.” But experts who spoke to WIRED say the commitments are half measures. “There's not going to be a really simple yes or no on whether something is AI-generated or not, even with watermarks,” says Sam Gregory, program director at the nonprofit Witness, which helps people use technology to promote human rights. Watermarking is commonly used by picture agencies and newswires to prevent images from being used without permission—and payment. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg But when it comes to the variety of content that AI can generate, and the many models that already exist, things get more complicated. As of yet, there is no standard for watermarking, meaning that each company is using a different method. Dall-E, for instance, uses a visible watermark (and a quick Google search will find you many tutorials on how to remove it), whereas other services might default to metadata, or pixel-level watermarks that are not visible to users. While some of these methods might be hard to undo, others, like visual watermarks, can sometimes become ineffective when an image is resized. “There's going to be ways in which you can corrupt the watermarks,” Gregory says. The White House’s statement specifically mentions using watermarks for AI-generated audio and visual content, but not for text. There are ways to watermark text generated by tools like OpenAI’s ChatGPT, by manipulating the way that words are distributed, making a certain word or set of words appear more frequently. These would be detectable by a machine but not necessarily a human user. That means that watermarks would need to be interpreted by a machine and then flagged to a viewer or reader. That’s made more complex by mixed media content—like the audio, image, video, and text elements that can appear in a single TikTok video. For instance, someone might put real audio over an image or video that's been manipulated. In this case, platforms would need to figure out how to label that a component—but not all—of the clip had been AI-generated. And just labeling content as AI-generated doesn’t do much to help users figure out whether something is malicious, misleading, or meant for entertainment. “Obviously, manipulated media is not fundamentally bad if you're making TikTok videos and they're meant to be fun and entertaining,” says Hany Farid, a professor at the UC Berkeley School of Information, who has worked with software company Adobe on its content authenticity initiative. “It's the context that is going to really matter here. That will continue to be exceedingly hard, but platforms have been struggling with these issues for the last 20 years.” And the rising place of artificial intelligence in the public consciousness has allowed for another form of media manipulation. Just as users might assume that AI-generated content is real, the very existence of synthetic content can sow doubt about the authenticity of any video, image, or piece of text, allowing bad actors to claim that even genuine content is fake—what’s known as the “liar’s dividend.” Gregory says the majority of recent cases that Witness has seen aren’t deepfakes being used to spread falsehoods; they’re people trying to pass off real media as AI-generated content. Business What Sam Altman’s Firing Means for the Future of OpenAI Steven Levy Business Sam Altman’s Sudden Exit Sends Shockwaves Through OpenAI and Beyond Will Knight Gear Humanity’s Most Obnoxious Vehicle Gets an Electric (and Nearly Silent) Makeover Boone Ashworth Security The Startup That Transformed the Hack-for-Hire Industry Andy Greenberg In April a lawmaker in the southern Indian state of Tamil Nadu alleged that a leaked audio recording in which he accused his party of stealing more than $3 billion was “machine-generated.” (It wasn’t.) In 2021, in the weeks following the military coup in Myanmar, a video of a woman doing a dance exercise while a military convoy rolls in behind her went viral. Many online alleged that the video had been faked. (It hadn’t.) Right now, there’s little to stop a malicious actor from putting watermarks on real content to make it appear fake. Farid says that one of the best ways to guard against falsifying or corrupting watermarks is through cryptographic signatures. “If you're OpenAI, you should have a cryptographic key. And the watermark will have information that can only have been known to the person holding the key,” he says. Other watermarks can be at the pixel level or even in the training data that the AI learns from. Farid points to the Coalition for Content, Provenance, and Authenticity , which he advises, as a standard that AI companies could adopt and adhere to. “We are quickly entering this time where it's getting harder and harder to believe anything we read, see, or hear online,” Farid says. “And that means not only are we going to be fooled by fake things, we're not going to believe real things. If the Trump Access Hollywood tape were released today, he would have plausible deniability,” Farid says. 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All rights reserved. Use of this site constitutes acceptance of our User Agreement and Privacy Policy and Cookie Statement and Your California Privacy Rights. WIRED may earn a portion of sales from products that are purchased through our site as part of our Affiliate Partnerships with retailers. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Condé Nast. Ad Choices Select international site United States LargeChevron UK Italia Japón Czech Republic & Slovakia "